Section Completed
Operational Intelligence for the Modern Logistics & Supply Chain Enterprise — the single source of truth for every Certin team member. Earn XP as you read.
$9.5T market, verticals, the gap
Six failure modes, €1M in preventable loss
Central nervous system architecture
8 core modules, the push advantage
Who buys, buyer maps, trigger signals
6 categories, the compounding moat
Before/after outcomes, business KPIs
ICP → Outreach → Discovery → Close
Tens of thousands of mid-market logistics operators are flying blind — losing millions in preventable SLA penalties, managing operations through disconnected tools, watching ghost packages consume their credibility with clients who deserve better. They know the problem. They are ready for the solution.
The question is only whether Certin is the company that delivers it. We are.
Logistics and supply chain is the circulatory system of the global economy. Every product has passed through a network of warehouses, vehicles, information systems, and human decisions before reaching you.
Certin operates across four core logistics verticals, each with distinct pain profiles, regulatory requirements, and entry points into the organisation.
Omnichannel fulfillment, seasonal volume spikes, vendor compliance with major retailers, managing returns at scale. Strict SLA requirements with significant financial penalties for non-compliance. Chargebacks from retailer VCP failures are a primary financial pain point — a missed delivery window at a major supermarket chain can result in deductions of 15–25% of the invoice value.
Same-day/next-day delivery expectations driven by Amazon consumer norms. Last-mile representing 40–53% of total costs. High volumes at extremely thin margins — zero tolerance for error. Real-time tracking API requirements, ghost packages a critical credibility issue. A single ghost package complaint on a major platform can trigger account performance reviews affecting entire business relationships.
Thousands of SKUs, shelf-life constraints, promotional planning cycles, coordinating hundreds of third-party providers. Data lives in a dozen systems — ERP, TMS, distributor portals, spreadsheets. Promotional execution failures mean wasted production runs, retail out-of-stocks, and forfeited promotional investment. Distributor performance gaps are opaque until a retailer calls to complain.
Temperature-controlled infrastructure for pharma, food, and biologics. GDP compliance requirements, highest-cost logistics infrastructure. A single undocumented temperature excursion can render an entire batch non-compliant and force a recall. Regulatory audit readiness is existential — not a nice-to-have. Cold-chain operators face the highest consequence of any vertical for operational data gaps.
Our ideal customer operates with 50–200 field staff, generates €20M–€200M in annual revenue, and runs 5–50 locations or depots. Large enough to experience genuine operational complexity and SLA exposure — with meaningful contract penalty structures. Small enough that enterprise platforms from SAP, Oracle, or Manhattan Associates are completely prohibitive in both cost and implementation overhead.
This segment has been systematically ignored by every major technology player. Enterprise TMS platforms start at implementation costs that exceed most mid-market annual IT budgets. Generic BI tools require data engineers. This is Certin's opening — and it is wide.
SAP TM, Oracle TM, Manhattan Associates — built for 500+ staff with dedicated IT teams. 6–18 month implementations. Annual licensing in the hundreds of thousands. Mid-market operators cannot absorb the cost, the implementation timeline, or the organisational change management burden that comes with these platforms.
Power BI, Tableau, Looker — powerful but requiring dedicated data engineers to configure and maintain. Zero logistics domain knowledge built in. Weeks of implementation. Pull-based reporting that cannot operate in real time for a shift controller managing 200 live deliveries simultaneously across multiple carriers.
The true incumbent solution for the mid-market. Free, familiar, and deeply embedded in operational culture across European logistics. But manually error-prone, unscalable, and the direct root cause of ghost packages, SLA breaches, and shift handover failures. This is what Certin displaces — not software, but manual habit.
The root cause is data fragmentation. Operations teams manage businesses across 10+ disconnected systems: TMS, WMS, ERP, Excel, WhatsApp, Email, GPS, Scanners, EDI, and phone calls — with no unifying intelligence layer.
These are not isolated bugs — they are interconnected symptoms of a structurally broken information architecture. Each one feeds and amplifies the next. Together they create an operational environment that is perpetually reactive, chronically behind, and systemically unable to prevent the problems it keeps suffering.
Between 8–20% of packages lose all traceability mid-journey. No delivery confirmation scan, no exception event, no GPS ping. The package simply disappears from every system simultaneously. Investigation requires manually cross-referencing TMS, carrier portals, driver WhatsApp threads, and GPS history — a 2–4 hour process per incident. At 50+ incidents per week across a mid-size operation, this is not an edge case. It is a structural operational drain consuming entire working weeks of controller time, every week, indefinitely.
2–4 hrs detection → 5 mins with CertinSLA breach penalties run 15–25% of contract value. The critical problem: breaches are only discovered after they occur — via a customer complaint email or a disputed invoice. There is no forward-looking signal. No system flags that a delivery is heading for breach 4 hours before the cutoff. Controllers cannot intervene because they have no visibility into developing risk. The entire SLA management regime is reactive, not preventive — meaning every avoidable penalty is not avoided, and every winnable customer conversation is instead a damage control exercise.
€300K–€1M lost annually per mid-market operatorWhen a shift ends, 60–80% of contextual operational knowledge evaporates. The outgoing supervisor holds context about a delayed carrier, an escalated customer complaint, a vehicle breakdown, and an open exception — none of which is systematically captured in any system. The incoming shift spends its first 30–60 minutes reconstructing the operational picture from WhatsApp threads, sticky notes, and verbal briefings. This is not a process problem. It is a structural data loss problem — and it is repeated across every site, every day, every handover, indefinitely.
30–60 mins reconstruction per shift start25–35 hours per week are consumed by report compilation across a typical mid-market operation. Daily performance summaries, SLA compliance reports, carrier scorecards, and KPI dashboards are built manually by extracting data from multiple systems, reformatting in Excel, and distributing by email. This is equivalent to losing one full-time FTE per site to administrative overhead — every week, every year. Every report is backward-looking. None are predictive. And the person who produces them cannot simultaneously be monitoring live operations for emerging risk.
~1 FTE per site consumed by reporting overheadWhen a customer disputes a delivery outcome, the logistics operator must compile a complete evidence package: every scan event, GPS checkpoint, driver communication, customer contact record, and system timestamp. This takes 2–4 hours per dispute. With 50–200 disputes per month across a mid-market operator, hundreds of hours per month are consumed by evidence hunting. Incomplete or fragmented records mean many winnable disputes are conceded — not because the operator was wrong, but because the evidence cannot be assembled quickly enough to counter the claim before the billing cycle closes.
50–200 disputes/month × 2–4 hrs eachOperations do not stop at 6pm. Drivers encounter problems overnight. Carriers report exceptions. Customers escalate issues on delivery platforms. But after business hours, there is no structured intake mechanism. Incidents are partially documented or not at all. SLA breach risk accumulates overnight without any human or system intervention. The incoming morning shift inherits a backlog of unresolved escalations and undocumented problems — starting each day in catch-up mode before the first phone call is even made.
Risk accumulates nightly without interventionFor a mid-market operator generating €20M in annual revenue at industry-average SLA breach rates of 15–20%, the combined annual financial impact across penalties, ghost package investigation, manual reporting overhead, and lost disputes is €300,000–€1,000,000 per year in fully preventable losses. At 2–5% of revenue, this is not merely inconvenient. It is existential.
Certin is an intelligent operational platform built natively for mid-market logistics and supply chain operators. It transforms fragmented, disconnected operational data into unified, real-time intelligence — and acts on it.
Certin connects every data source in a logistics operation — TMS, WMS, ERP, GPS, scanners, WhatsApp, email, EDI, Excel — applies AI to detect anomalies and predict risk in real time, and delivers actionable intelligence to operators via the channels they already use, before problems become penalties.
The metaphor that best captures what Certin is: a central nervous system for logistics operations. The human body's nervous system doesn't store information in one place — it aggregates signals from every organ, identifies patterns, and triggers responses automatically. Certin does the same for logistics operations.
Native connectors to TMS, WMS, ERP. API integrations with carrier systems and GPS platforms. EDI parsing (EDIFACT, X12, custom formats). File-based ingestion (Excel, CSV, FTP drops). Email and communication monitoring (WhatsApp Business API, inbound email parsing). Scanner event streams from handheld and fixed devices. The data layer is technology-agnostic — it meets operators where they are, not where they wish they were. No system replacement required. No 6-month implementation project. Connection and configuration in days.
All sources → single unified event streamSix AI agents run in continuous concert: Signal & Correlation Agent correlates events across all sources to identify emerging patterns before they surface as problems. Risk & Anomaly Agent scores every active delivery for breach probability using predictive modelling. Investigation Agent autonomously reconstructs timelines when anomalies occur. Action & Workflow Agent triggers escalations and automated communications. Finance & Margin Agent tracks penalty exposure and contract adherence in real time. Communication Agent parses inbound messages, classifies intent, and routes to action.
Continuous real-time processing across all agentsIntelligence is never passive. Certin does not wait for users to log into a dashboard and find the right signal. It pushes structured, actionable intelligence to operators via WhatsApp, email, and voice — wherever they are, on whatever device they are using. An operations controller on the depot floor receives an SLA risk alert on WhatsApp 5 hours before the potential breach. A depot manager receives an AI-generated shift handover briefing before their shift begins. A finance lead receives a fully compiled dispute evidence package in under 5 minutes.
No dashboards required — intelligence comes to the operatorEvery resolved incident, every confirmed prediction, every operator feedback signal feeds back into Certin's models. Root cause validation confirms or refines predicted causes of each exception type. False positive incidents recalibrate sensitivity thresholds. SLA prediction models update continuously with new route and carrier performance data. Escalation routing improves with every outcome signal. The system becomes progressively more aligned with each specific operation's patterns, routes, contracts, and carrier behaviours — creating a compounding intelligence advantage that deepens with every week of operation.
Models compound in accuracy with every resolved incidentTraditional BI tools assume users will regularly log in, navigate to the right view, and notice the right signal. In a live operation managing 200 active deliveries across multiple depots and carriers simultaneously, no operations controller has time or bandwidth to monitor dashboards. The signal is lost in the noise. Problems surface as crises rather than alerts. Dashboards are retrospective tools masquerading as operational ones.
Certin inverts this entirely. The system monitors everything continuously and surfaces only the signals that require action — delivered directly to the operator's attention channel with context, recommended action, and urgency rating already embedded. The operator does not need to seek out the intelligence. The intelligence arrives in the same place they are already working: their WhatsApp, their email, their phone.
Because Certin delivers through channels operators already use daily — WhatsApp especially in EU logistics operations — there is near-zero new behaviour required. No login. No new interface to learn. No resistance from field staff who are too busy to engage with another platform. The intelligence arrives in the same thread as driver communications. Adoption is immediate because behaviour change is minimal. This is not a coincidence of design — it is the core architectural choice.
Certin is built on a dual-track architecture: backward-compatible with how the logistics industry actually operates today, and forward-engineered for where it is going. We don't ask operators to change their world to use us — we meet them in it.
Building only the AI layer means you cannot onboard any customer whose data lives in EDI systems — which is most of the mid-market. Building only the EDI layer means you have data but no intelligence. The combination is Certin's architectural moat: we ingest the data the industry already produces and apply AI that the industry couldn't previously afford or build. Neither layer alone is the product. Together, they are.
Continuously correlates expected movement events against actual received events for every active shipment. When a package fails to produce an expected scan, GPS ping, or delivery confirmation within a defined window, Certin's Investigation Agent triggers an autonomous cross-reference across carrier systems, driver location data, and communication history.
Resolution path is generated automatically and delivered to the relevant controller via WhatsApp or email. What previously took 2–4 hours of manual detective work across 7 systems now resolves in under 5 minutes. Detection happens before the customer calls.
Detection: <5 min vs 2–4 hr industry standardUses predictive modelling to identify breach risk 4–6 hours before a potential SLA breach event. Inputs: current delivery location, historical lane and carrier performance, real-time traffic and weather, vehicle load, and historical SLA performance for the same route/day/time window.
When breach probability exceeds threshold, Certin's Action & Workflow Agent triggers an automatic escalation to the controller, an optional early customer notification, and a recommended intervention path. Operators move from reactive penalty management to proactive risk elimination.
4–6 hr advance warning vs post-breach discoveryAI-generated handover briefings compiled in under 2 minutes at shift close. Covers: open exceptions and current status, active SLA risks for the incoming shift window, carrier and driver communications requiring action, pending customer escalations with full contextual history, and a priority action list ranked by urgency and financial exposure.
The incoming supervisor receives a complete operational picture before taking their first call. 30–60 minutes of reconstruction time is eliminated. Operational continuity becomes structural — not personality-dependent.
2 min briefing vs 30–60 min reconstructionWhen a customer raises a dispute, Certin's "Timeline of Truth" compiles automatically in under 5 minutes: every scan event, GPS checkpoint with timestamp, driver communication, customer contact event, system state change, and carrier acknowledgement.
The evidence package is structured, formatted, and ready to share. Previously-conceded disputes become winnable because the evidence that was always there — buried across 7 systems — is now instantly assembled and verifiable.
<5 min vs 2–4 hr evidence compilationDaily performance summaries, SLA compliance reports, carrier scorecards, exception trend analyses, and custom KPI reports — all generated automatically and distributed on schedule. Configured once; runs permanently. The 25–35 hours per week consumed by manual report compilation are reduced by 80%.
Powered by Certin's conversation analytics layer — the same PostgreSQL persistence that stores every interaction also generates the structured insight that feeds every report automatically.
80% reduction in manual reporting timePlain language queries against the full operational dataset — no SQL, no BI configuration, no analyst required. Powered by intent classification and dynamic tool binding built into the agent workflow. The system understands logistics domain vocabulary natively.
"Show me all deliveries delayed more than 2 hours in the North East today." "Which carriers have the worst SLA performance this week?" "What is the current exception rate for the Tesco account?"
Domain-native NLP — no SQL or BI requiredAn always-on intake agent handling inbound calls, WhatsApp messages, and emails when human operators are unavailable. Captures structured incident details, cross-checks current system state to assess severity, classifies priority, and triggers appropriate workflows — without human involvement.
Every incident receives a complete audit trail regardless of when it occurs. The morning shift inherits a fully documented, triaged incident list — not a pile of unanswered messages and undocumented problems.
24/7 structured intake with full audit trailEvery resolved incident feeds back into Certin's models. Root cause validation confirms or refines predicted exception causes. False positives recalibrate sensitivity thresholds. SLA prediction models update continuously. The system becomes more accurate and more contextually relevant with every week of operation at each customer site.
This is the compounding moat. No competitor can replicate 12 months of learned operational context for a specific network. The value of replacing Certin grows with every month it runs.
Intelligence compounds in accuracy over timeThis section is for everyone building Certin. Here's an honest, detailed picture of where the AI backend is, what it can do, and how each component connects to the product we're selling.
The team has successfully implemented a robust multi-agent AI system with enterprise-grade features: intelligent LLM orchestration with multi-provider fallback, persistent conversation memory across sessions, a comprehensive tool ecosystem for database operations and analytics, and real-time system health monitoring. Both Phase 1 (core infrastructure) and Phase 2 (enhanced capabilities) are complete. The foundation is production-ready.
The foundational architecture for the multi-agent system using LangGraph. This is the skeleton that every other capability runs on. If you're building features, you're building on top of this.
| Component | What it is in plain English | Why it matters for the product |
|---|---|---|
| graph.py Agent Workflow Graph | Defines how agents connect and hand off to each other. The map of who does what and when. | This is the brain stem. Every user query flows through this graph — intent identified, right agent invoked, response returned. |
| llm.py LLM Provider Management | Manages connections to OpenAI, Gemini, and Groq. If one fails, another takes over automatically. | Ensures the intelligence layer never goes dark. No single vendor outage can take down the system. |
| memory.py Conversation Persistence | Stores every conversation in PostgreSQL. When a user comes back, the system remembers where they left off. | This is what makes Certin feel like a co-worker rather than a search engine. Context carries across sessions. |
| nodes.py Workflow Processing Nodes | The individual processing steps — classify the intent, execute the right tool, coordinate multi-step tasks. | Intent routing lives here. "Show me delayed deliveries" goes to the right tool because nodes.py figured out what the user actually wanted. |
| state.py State Management Schema | Defines the shape of data that flows through the workflow. Strongly-typed so bugs get caught early. | Prevents silent failures. Every piece of data passing through the agent graph is validated at the state level. |
| tools.py Tool Integration Framework | The capabilities the agent can use — database queries, analytics, BI functions. Each "tool" is a defined action the AI can invoke. | This is the bridge between AI reasoning and real operational data. The AI decides to query the database; tools.py makes it happen. |
| agent_repo.py Data Repository Layer | Full CRUD for agent data — conversations, messages, state checkpoints for workflow recovery. | If the system crashes mid-workflow, it can recover. Message history is retrievable. Audit trails are persistent. |
The second phase expanded what the system can do and hardened how reliably it does it. These are the features that make the difference between a demo environment and a production system.
Priority chain: OpenAI → Groq → Gemini. Intelligent fallback means continuous availability. Provider diversity removes the single-vendor dependency risk. In practice: if OpenAI goes down, users won't notice. Groq picks up the workload with near-identical response quality.
Model instance caching eliminates the initialisation overhead that previously added latency to every query. The improvement is measurable: up to 60% reduction in response times under load. For a shift controller handling exceptions in real time, this matters — every second of delay is a second closer to a breach.
Sophisticated intent classification routes queries to the right agent with high accuracy. Dynamic agent type determination based on full context — not just keywords. Multi-intent queries are handled. A single message that's half-report request and half-exception query goes to both agents and synthesises a combined response.
Dynamic tool binding based on agent context — agents don't just pick from a fixed list of tools, they select tools appropriate to their current context and the user's specific request. Streamlined execution pipeline reduces the round-trip time between "user sends query" and "user receives actionable response."
Comprehensive health check endpoint with real-time status reporting across all providers. Groq API key validation. Proactive issue detection. The operations team can see system state at a glance — no more finding out about provider failures from user complaints. This is 99.9% uptime architecture in practice.
Class-based authentication middleware — cleaner, testable, extensible. New database models for conversation management with rich metadata. Schema optimised for high-volume message handling and complex analytics queries. ACID compliance via PostgreSQL ensures no conversation data is ever lost or corrupted.
| Tech Stack Component | Role in the System | Business Implication |
|---|---|---|
| LangGraph | Multi-agent workflow orchestration — the framework that coordinates all agents, manages state transitions, and enables complex multi-step processing | Enables the kind of compound, context-aware reasoning that makes Certin feel intelligent rather than scripted |
| PostgreSQL | Persistent storage for all conversations, messages, and state checkpoints. ACID-compliant, enterprise-grade reliability | Every interaction is auditable. Conversation history powers personalisation. No data is ever lost — which matters enormously for dispute evidence |
| OpenAI / Groq / Gemini | Three LLM providers with intelligent priority-based routing and automatic failover | High availability. Cost optimisation (Groq is significantly cheaper for suitable workloads). No single-vendor lock-in |
| Python / FastAPI | Primary backend language and API framework — well-understood, well-supported, fast iteration | The team can move quickly. External integrators have standard REST API access. SwaggerUI available for testing |
The items below represent our near and medium-term priorities. Completed items are marked. Coming items have checkboxes — tick what you've personally verified or feel confident about. Your checks are saved only on this device and are never shared. Use this as your own running view of where we are.
Certin targets the mid-market logistics operator caught between enterprise software that is too complex and consumer tools that are too simple. This segment is large, structurally underserved, and financially motivated to act.
| Dimension | Specification | Notes |
|---|---|---|
| Company Type | 3PL operator, regional logistics provider, in-house logistics arm of FMCG/Retail | Both asset-based and non-asset carriers |
| Field Staff | 50–200 operations and field staff | Enough complexity; under-resourced for enterprise tech |
| Site Footprint | 5–50 depots, warehouses, or operational locations | Multi-site coordination creates the fragmentation problem |
| Annual Revenue | €5M – €100M | SLA exposure meaningful; ROI on Certin clearly calculable |
| Verticals | Retail logistics, e-Commerce, FMCG, Cold-Chain | See vertical entry points in GTM section |
| Geography | Phase 1: FR, BE, UK · Phase 2: DE, NL, ES, IT · Phase 3: GCC, West Africa | Expansion sequenced by market maturity and founding team expertise |
| Technology Stack | 1–3 core systems (TMS/WMS/ERP) + heavy Excel and WhatsApp | Under-resourced IT teams; no in-house data engineering |
| Pain Trigger | Recent SLA penalty, contract non-renewal, ghost package crisis, excessive reporting load | Event-driven buying — pain must be active, not theoretical |
| Qualification Signal | Active SLA obligations · multi-system environment · ops team of 3+ | Disqualify: under 50 employees, no SLA structure, enterprise 500+ IT team |
Every deal involves multiple stakeholders. Understanding the motivation, language, and decision power of each persona is required for successful selling and sustained adoption.
Accountable for full P&L. Experiences the problem in financial terms: SLA penalty charges, contract non-renewals, operational scaling costs. Has budget authority. Will sign the contract but needs ROI certainty before committing. Responds to commercial metrics, payback periods, and case studies — not feature lists.
Manages day-to-day site performance. Drowning in manual coordination, ghost package investigations, and shift handover gaps. Most motivated user in the organisation. Likely to become an internal champion once they see the product work. Best source of product insight during design partner phase.
Managing 50–200 active deliveries simultaneously. Triaging exceptions in real time. Needs speed and clarity above all else. Time-to-alert is their key metric. Will adopt immediately if Certin reduces noise and surfaces the right signals. WhatsApp delivery is non-negotiable for this persona.
Coordinates driver schedules, manages live route changes, handles inbound carrier calls throughout the shift, and is typically the first person to learn when something goes wrong in the field. Often holds critical real-time information that never makes it into any system — a driver flagging a delay verbally, a collection missed before it hits the TMS. Certin gives this person a structured place to log, escalate, and close the loop on incidents that would otherwise fall through the gap between what happened and what was recorded.
Spends disproportionate time compiling evidence for customer disputes and SLA compliance reports. Responsible for penalty tracking and dispute outcomes. One-click evidence packages and automated compliance reporting are transformative for this persona. Quantifiable time savings are the primary buying signal.
Evaluates technical feasibility, data security architecture, integration complexity, and ongoing maintenance requirements. Under-resourced. Resistant to anything that adds to maintenance burden. Certin's plug-and-play approach, GDPR compliance, and API-only integration are the critical technical messages for this persona.
A recent or ongoing penalty charge from a major retail or e-commerce customer. The financial pain is quantified, current, and personal to the decision-maker. Best trigger for an immediate ROI-anchored conversation. The prospect already knows the cost; Certin just quantifies the solution.
A customer has cited operational performance issues in a renewal discussion. The existential risk of losing the revenue concentrates decision-maker attention and dramatically accelerates the buying process. This is the highest-urgency trigger in the portfolio.
A major customer has escalated a ghost package situation to senior management. The reputational and financial exposure makes the prospect motivated to solve the underlying problem systematically — not just this individual incident.
The operations team is spending so much time on report generation that they have flagged it to leadership as unsustainable. The hiring cost of additional headcount to handle reporting creates a clear ROI frame for automation investment.
Certin does not compete directly with any single category. It synthesises capabilities that exist only in fragments across six distinct competitive categories — each of which fails the mid-market operator in a different way.
| Category | What They Do Well | Why They Fail Mid-Market |
|---|---|---|
| Enterprise TMS SAP, Oracle, Manhattan | Deep transport planning, carrier management, full process automation for large enterprises with dedicated IT teams | Designed for 500+ staff. 6–18 month implementations. Costs exceed mid-market annual IT budgets. Requires dedicated internal IT/data teams. Zero AI insight layer on top of the core TMS functionality. |
| Mid-Market TMS Trimble, Descartes, Centiro | Reasonable transport management for mid-market. Carrier connectivity. Route planning functionality. | Operational systems only — not intelligence platforms. Siloed from non-transport data. No cross-system intelligence. No NLP. No SLA prediction. No ghost package detection capability. No push-based delivery. |
| General BI / Analytics Power BI, Tableau, Looker | Powerful data visualisation. Flexible and widely understood in enterprise contexts. Strong integrations. | Require data engineers and weeks of configuration. Pull-based and passive. No logistics domain knowledge. No EDI understanding. No predictive capability. No automated actions or escalations. |
| Freight Visibility Project44, Fourkites, Shippeo | Real-time multimodal freight tracking. Large carrier network integrations. Enterprise-grade SLAs. | Focus exclusively on freight location — not operational intelligence. No depot/warehouse integration. No SLA prediction beyond ETA. No NLP. No reporting automation. Enterprise pricing minimum commitments. |
| WMS Platforms Manhattan, Blue Yonder, Infor | Deep warehouse management. Inventory accuracy. Putaway and pick optimisation. | Warehouse-only scope. No outbound transport visibility. No customer SLA management. No carrier data integration. Siloed by design and cannot provide the cross-system view Certin delivers. |
| Spreadsheets + WhatsApp Excel, Google Sheets, WhatsApp | Free. Familiar. Immediately deployable. Deeply embedded in mid-market European logistics operations culture. | The incumbent "solution" — and the root cause of every problem Certin solves. Manually error-prone, unscalable, structurally generates ghost packages and SLA breaches, makes shift handover information loss inevitable. |
| Capability | CERTIN | Ent. TMS | Mid TMS | BI Tools | Visibility |
|---|---|---|---|---|---|
| Ghost Package Detection | ✓ | ✗ | ✗ | ✗ | ~ |
| Predictive SLA (4–6hr advance) | ✓ | ✗ | ✗ | ✗ | ✗ |
| AI Shift Handover Briefing | ✓ | ✗ | ✗ | ✗ | ✗ |
| 1-Click Dispute Evidence | ✓ | ~ | ✗ | ✗ | ✗ |
| Natural Language Query | ✓ | ✗ | ✗ | ~ | ✗ |
| Push Alerts (WhatsApp/Email) | ✓ | ✗ | ~ | ✗ | ~ |
| Deep EDI Parsing | ✓ | ✓ | ~ | ✗ | ~ |
| After-Hours Intake Agent | ✓ | ✗ | ✗ | ✗ | ✗ |
| Automated Report Generation | ✓ | ~ | ✗ | ~ | ✗ |
| Mid-Market Pricing | ✓ | ✗ | ~ | ~ | ✗ |
| Same-Day Deployment | ✓ | ✗ | ✗ | ✗ | ✗ |
| Adaptive Learning | ✓ | ✗ | ✗ | ✗ | ✗ |
✓ Full capability · ~ Partial/limited · ✗ Not available
Certin's competitive advantage is not static. The longer it operates within a logistics network, the more it understands the specific routes, contracts, carrier behaviours, and failure patterns of that network. Predictions become more accurate. Anomaly detection becomes more precise. Baseline performance models become more granular. The value of replacing Certin with a generic alternative increases with every month of operation — because no alternative can replicate 12 months of learned operational context.
This compounding intelligence moat is the core reason we target 120%+ Net Revenue Retention. Customers do not just stay — they expand, because the system becomes more valuable over time.
Two sets of metrics define Certin's success: operational outcomes for customers (the value we deliver), and business metrics for Certin (how we scale that value into a durable company).
Against a Certin annual fee of €40,000–€60,000, the annualised benefit for a €20M revenue operator is approximately €275,000–€360,000. That represents a 4–6× ROI in the first year, achievable within 60 days of deployment. This is the ROI conversation that closes deals.
Certin's initial market entry is a single, focused use case: Predictive Exception & SLA Rescue for mid-market 3PL operators under active penalty exposure. We do not start with the full platform. We start with the problem that is on fire right now.
We sell penalty reduction to operations directors already being asked why SLA breach costs are rising — and we prove the value in days, not quarters.
| Criteria | Why SLA Rescue Qualifies |
|---|---|
| Pain is pre-quantified | The prospect's finance team already has the penalty number. We are not asking them to calculate a hypothetical — we are solving a cost they are already tracking. |
| Buying moment is identifiable | Trigger signals are observable through outbound research: penalty charge, contract renewal at risk, SLA complaint from a key account, new ops hire. |
| Time to value is fast | Predictive SLA alerts require data integration only — no process change, no staff training. The operator sees a result within the first week. |
| ROI is verifiable | Every breach warning and every penalty avoided can be attributed directly to Certin. The before/after is measurable in the same financial terms the prospect was already using. |
| Platform expansion is natural | Once the SLA Sentinel is live, data infrastructure and trust are in place. Ghost Package Prevention, Shift Orchestration, and Reporting all extend the same integration — not a new deployment. |
| It generates the proof asset | Every design partner who avoids a penalty or resolves a ghost package in 5 minutes is a case study. The wedge creates the commercial proof that unlocks the broader market. |
Every day Certin runs inside a design partner's operation, it learns that operator's specific network — their carrier patterns, SLA risk profiles, exception types. By the time the commercial conversation begins, Certin already knows their operation better than any generic platform could after months of configuration. The wedge is not just a sales entry point. It is the beginning of the data moat that makes Certin irreplaceable.
Phase 1 builds design partnerships and validates product-market fit. Phase 2 converts that proof into commercial customers at launch. The entire system runs at zero tooling cost.
Identify and secure 5–10 logistics operations teams as committed design partners. Co-build the product around their real workflows. Validate ICP, messaging, and core value props with zero commercial pressure. Every week of this phase generates product intelligence and customer proof simultaneously.
Commercial launch using design partner case studies as proof. Convert partners to paying customers at locked preferential terms. Scale outbound with refined messaging rooted in real outcomes. Add inbound via LinkedIn content and referral programme. Every case study is a sales asset; every reference customer is a multiplier.
| Phase | Markets | Timeline | Rationale |
|---|---|---|---|
| Phase 1 | France, Belgium, United Kingdom | Now — 18 months | Strong 3PL ecosystems. High FMCG and retail concentration. Established founding team relationships. French market depth provides initial pipeline density. UK provides EDIFACT standardisation validation. |
| Phase 2 | Germany, Netherlands, Spain, Italy | 18 – 36 months | Largest EU logistics markets by volume. e-Commerce growth driving 3PL demand surge. EDIFACT EDI standard alignment. German mid-market 3PL sector is particularly underserved by current tooling options. |
| Phase 3 | UAE, Saudi Arabia, Nigeria, Ghana | 36 months+ | Emerging markets with acute logistics data fragmentation. Rapidly growing 3PL sector. Severely underserved by current solutions. GCC provides high-value early adopters; West Africa represents long-term volume opportunity. |
Mid-sized logistics and 3PL operations teams who have enough scale to feel the pain of fragmented data acutely — but not the engineering budget to build custom solutions. The buying decision is event-driven.
Type: 3PL, freight forwarder, last-mile carrier, supply chain operator
Size: 50–500 employees · £5M–£100M revenue
Geography: UK (primary) · Ireland, Netherlands, Germany, UAE (secondary)
Ops Scale: 500+ shipments/day or 10+ clients concurrently managed
Tech Stack: TMS + WMS + spreadsheets + email + WhatsApp, typically 1–3 core systems with heavy manual overlay
Must Have: Active SLA obligations · multi-system data environment · ops team of 3+
Strong Fit: Using legacy TMS (SAP, Oracle, Cargowise) · recent ops headcount growth · control tower function exists
Bonus Signals: New senior ops hire · recent funding round · job posting for data analyst role
Disqualify If: Under 50 employees · no SLA structure · owner-operated single-driver outfit · enterprise with 500+ IT team already in place
Company posted about delivery delays or SLA issues on LinkedIn or in trade media. Active pain, active stakeholder, high receptivity to a solution conversation right now.
A job ad for "Operations Analyst" or "Data Analyst – Logistics" signals the company knows they have a data problem but is trying to solve it with headcount rather than tooling. Ideal moment to intercept with an alternative ROI frame.
Recent funding round means budget exists to invest in operational tools. Decision-makers are focused on scaling efficiently. Timing is optimal for a new platform adoption conversation.
A new VP/Head of Operations or similar has recently joined. New leaders typically have a mandate to improve and are actively looking for tools to demonstrate early impact. High receptivity window within the first 60–90 days of tenure.
All leads sourced manually via Apollo free tier, LinkedIn search, and Google Business. Zero tooling cost. Every prospect flows into the Google Sheets CRM immediately after identification.
Saved searches with ICP filters. Chrome extension for LinkedIn email lookup. Intent data signals to identify companies actively researching logistics software. Use "researching logistics software" intent filter for highest-priority prospects. Stay within 50 export/month limit by adding leads manually from search results rather than bulk exporting.
Manual prospecting via saved search strings. Connect + personalised message to ops decision makers. Profile intel for trigger events — new role, company news, post activity. 10–15 connection requests/day maximum. Follow up within 24 hours of accepted connection. Never pitch on the connection request itself.
Maps and search to find regional 3PLs and logistics operators not yet in Apollo databases. Particularly effective for finding depot-based operators who have minimal digital presence but active operations. Phone numbers and websites sourced directly. Best for smaller regional operators in the mid-market ICP band.
Master pipeline tracking all prospect data, status, notes, and follow-up dates across 6 tabs: Active prospects, Calls Booked, Design Partners, Disqualified, Daily Activity Log, Phase 2 Pipeline. Colour-code status column across 7 status categories. Update daily without exception — a stale CRM is a dead pipeline.
| Search Config | Keywords | Seniority Filter | Use Case |
|---|---|---|---|
| Config A — UK 3PL Ops | "3PL" OR "logistics" OR "supply chain" | Director, VP, C-Suite, Manager | Primary outbound — highest volume |
| Config B — Freight Forwarders | "freight forwarding" OR "freight forwarder" | Operations Director, COO, CEO | Secondary — strong SLA exposure |
| Config C — Control Tower | "control tower" OR "transport manager" OR "logistics manager" | Manager, Senior Manager | Champion identification — frontline pain |
| Config D — In-House Logistics Arms | "head of logistics" OR "VP supply chain" | VP, Director, Head of | FMCG/Retail in-house logistics teams |
Time-blocked daily activities run in parallel. Consistency beats volume. Same channels, every day, every week. The power of this system is not any single touchpoint — it is the compounding effect of systematic daily execution across all channels simultaneously.
Two sequences deployed via Brevo (300 free emails/day). Click any email to expand full body copy. Personalise bracketed variables before sending. Domain warming: +10 emails/day for 2 weeks before full volume.
Verify getcertin.ai domain with SPF, DKIM, DMARC records. Warm up for 2 weeks starting at 10 emails/day, +10/day each week until reaching 150/day. Create separate contact lists for Sequence A and Sequence B. Enable open and click tracking. Target open rate: 30%+. Target reply rate: 8–12%.
5 emails · 14 days · Target: Ops Director / COO · Objective: Book a 20-minute demo call
3 emails · 7 days · Trigger: Within 2 hours of discovery call · Objective: Secure design partner commitment
One goal: secure a design partner commitment or a clear, actionable next step toward one. Every minute is accounted for. Never leave a call without confirming what happens next.
"Walk me through what happens when a shipment goes at-risk today — from the moment the data shows something's wrong to when it gets resolved."
"How many different systems does your team check during that process — TMS, WMS, carrier portals, WhatsApp, spreadsheets?"
"Who owns exception resolution — is there a dedicated person, or does it fall to whoever notices first?"
"How often do you have SLA misses that could have been caught earlier if someone had been alerted faster?"
"What would it be worth to your team if exception resolution time was cut in half? Have you ever tried to put a number on the penalty exposure?"
"What tools have you tried or evaluated before in this space? What stopped you from adopting them — was it cost, complexity, or did they just not solve the right problem?"
This is genuinely low commitment — the framing should always reflect that. You are asking for their time and perspective in exchange for direct product influence.
One monthly 45-minute product session with the founding team
Honest, specific feedback on features as they're built
Access to beta platform from agreed start date
Optional: introductions to other ops leaders in their network
This is the value exchange. Be explicit and specific about what they receive. The commercial terms lock is often the most compelling element for senior buyers.
Direct input into the product roadmap — not a feedback form, a seat at the table
Priority access before the June 2026 commercial launch
Preferential commercial terms locked — no surprises at launch
A platform built around their actual workflow — not a generic ICP assumption
Phase 1 targets over 14 weeks. Track funnel health weekly. If calls held drops below 4/week after week 4, increase call volume or revise targeting criteria.
| Metric | Weeks 1–2 (Ramp) | Weeks 3–5 | Weeks 6–10 | Weeks 11–14 |
|---|---|---|---|---|
| New leads/week | 50–75 | 75–100 | 75–100 | 50–75 |
| Emails sent/week | 150 | 150 | 150 | 150 |
| Calls made/week | 50 | 50 | 50 | 50 |
| Discovery calls held | 1–3 | 3–6 | 5–10 | 3–6 |
| Design partner proposals | 0 | 1–2 | 2–4 | 2–3 |
| Partners committed | 0 | 0–1 | 1–2/week | 1–2/week |
Commercial launch activates in June 2026. Design partner proof becomes the foundation for all outbound, inbound, and referral activity.
Partnership agreements transition to commercial contracts. Partners are already using the product — lowest friction conversion path in the portfolio. Locked preferential terms make the decision easy.
2–3 case studies from design partners with real operational metrics. Exception reduction %, reporting time saved, SLA breach rate improvement. These are the primary outbound proof points for every Phase 2 conversation.
Ask each design partner: "Who else in your network has this problem?" One warm referral is worth 20 cold calls in conversion rate and sales cycle length. This is the highest-leverage activity in Phase 2.
Same channels, increased daily targets. 20–30 new leads/day. Upgrade Brevo if pipeline exceeds 100 active prospects simultaneously. Refined messaging rooted in real case study outcomes.
Weekly posts on ops visibility, SLA failures, exception management, ghost packages — domain content that builds inbound interest from the exact ICP being targeted outbound. Build authority ahead of and post-launch.
UK Warehousing Association, CILT, FreightExpo. Sponsorship, speaking, or direct networking. Warm market entry into a concentrated ICP audience where relationships carry disproportionate weight.
Click each objection to reveal the recommended response. Memorise these before calling — fluency builds credibility. Every objection is a buying signal in disguise.
Every tool in this GTM is free at the volumes required for Phase 1. No budget needed to run a professional, high-volume, systematic outreach operation from day one.
Transactional and sequence emails. Automation workflows for Sequence A and B. Open/click tracking built in. Contact list management for segmentation.
ICP-filtered prospect search. Chrome extension for LinkedIn email lookup. Intent data signals. Stay within 50 export/month by adding leads manually from search results.
Prospect research, connection requests, and follow-up messages. Saved search strings. Profile intel for trigger events and new role signals.
Full CRM replacement for Phase 1. 6 structured tabs covering every stage of the pipeline and daily activity logging. Update daily without exception.
Tens of thousands of mid-market logistics operators are flying blind. They are losing millions in preventable SLA penalties, managing operations through disconnected tools, and watching ghost packages consume their credibility with clients who deserve better. They know the problem. They are ready for the solution.
The question is only whether Certin is the company that delivers it. — getcertin.ai