We design the failure modes first.
Most AI projects fail not because of bad models — but because of wrong outcomes, ignored risks, and no adoption plan.
A process built from benchmarking 40+ models across real production conditions, feeding directly into Codesota's open registry.
Why AI projects keep failing the same way.
The pattern is consistent across industries. We have seen it, measured it, and built a process that does not repeat it.
No defined outcomes
Projects kick off with "discovery" before anyone agrees what success looks like in business terms.
Risk as afterthought
Teams rush to PoC before mapping failure modes. The expensive risks surface late.
Vendor claims, no data
Vendors benchmark on their own test sets. We run independent evaluations on standardised datasets.
No explainability
Confidence design happens after the model is built. Users don't trust the system and route around it.
Deployment = done
Projects "complete" at launch. No adoption tracking, no feedback loops, no measurement of impact.
No kill criteria
Without pre-defined stopping conditions, failing projects keep burning budget.
Outcome-first AI development.
Eight phases, end-to-end. Every phase has explicit cards, a named deliverable, and a hand-off into the next. Skim the index, then drop into any phase below.
Outcome Definition
Before any discovery, any model selection, any code — we define what success looks like in business terms. And what would make us stop.
One number that moves
What single metric proves this worked? Not "model accuracy" — actual business impact. Invoice processing time. Screening throughput. Error rate in production.
What does AI decide?
Recommend? Automate? Escalate? The boundary between AI and human decision is designed explicitly — not discovered later when something goes wrong.
Failure taxonomy
A false positive in PEP screening costs differently than one in document OCR. We map the asymmetry of errors before touching a dataset.
Pre-defined stopping conditions
What would make us recommend stopping? Defined upfront, in writing, signed by sponsor. Not a post-hoc rationalisation when budget is spent.
Risk Architecture
Map all the ways this can fail before building anything. We design failure modes first — then build systems that avoid them.
Garbage in, garbage out
Missing labels, distribution shift, adversarial inputs, PII exposure. We audit before training, not after the model underperforms.
Wrong approach, wrong metric
Accuracy on a benchmark vs. accuracy on your documents. We map the gap before committing to an architecture.
Legacy systems don't move
API limitations, latency constraints, data format mismatches — these kill production deployments. We surface them in Phase 1, not Phase 5.
Users route around systems they don't trust
Workarounds, shadow processes, "I just do it manually" — adoption failure is as terminal as technical failure. We design for it explicitly.
Archaeology
Not discovery. Forensic understanding of how work actually happens — the tribal knowledge, hidden rules, and workarounds that determine real success.
How they actually do it
Not how the process doc says they do it. We map the actual workflow — the manual checks, the copy-paste, the Excel file nobody admits exists.
What exists, what's lying
Schema vs. reality. Labels created by different people in different contexts. Historical data that reflects policy decisions that no longer apply.
Rules that live in people's heads
"Sarah always double-checks the Polish names." "These suppliers always format amounts wrong." If it's not in the system, it needs to be in the model.
Where the current system fails
Every workaround is a failure mode waiting to become a model bug. We document them before they become our problem.
Benchmark Selection
We don't choose models based on vendor claims or blog posts. We select from independently verified benchmark data — the same infrastructure that powers Codesota.
Not the vendor's test set
Every model we recommend has been evaluated on standardised datasets: ICDAR, SROIE, OmniDocBench, and domain-specific Polish document corpora.
Best for invoices != best for handwriting
We slice benchmarks by document type, language, and production conditions — not top-line accuracy scores that hide tail failures.
API cost per 1K pages matters
The fastest model and the cheapest model are rarely the same. We model cost, latency, and accuracy together against your actual volume.
When to own the model
Sometimes fine-tuning beats any API at 1/10th the cost. We've done it: RysOCR achieved 71% CER reduction on Polish documents. We know when it's worth it.
40+ models tracked across 9 datasets. When we recommend a model for your use case, we can show you exactly where it ranks, where it fails, and what the next-best alternative is. See the benchmarks →
Confidence Design
How humans will trust — or appropriately distrust — this system. Designed before the model is built, not bolted on after deployment.
How does it say "I don't know"?
A model that outputs a confident wrong answer is worse than one that flags uncertainty. We design the confidence interface as a first-class deliverable.
Right level, right audience
The compliance officer needs different explainability than the data engineer. We design both — not a generic "show the attention map" non-answer.
Override is a feature, not a failure
When can humans override? What's logged? How does that feed back into the model? We treat every override as a training signal.
Prevent over-trust and under-trust
Both failure modes are real. A user who blindly accepts all outputs is as dangerous as one who ignores them. We design for the middle.
Learn Fast
Test the riskiest assumptions with minimum investment. Prove business value, not just technical feasibility. This phase exists to give you a real Go/No-Go.
Start with the riskiest thing
The assumption most likely to kill the project gets tested first — not saved for Phase 5 discovery when half the budget is gone.
Can we get the data we need?
Volume, quality, labelling cost, legal constraints. Many projects die here. Better to know in week two than month six.
Can any model hit required accuracy?
Against the threshold defined in Phase 0. Not "it looks promising" — a real number against a real bar.
Does it change behaviour?
Test with real users. A model that hits 94% accuracy but gets routed around by every operator has failed. Behaviour change is the real metric.
Engineer & Embed
Build the production system and ensure organisational adoption run in parallel. A model that works but nobody uses is a failure.
Production-grade MLOps
Training pipelines, monitoring, testing, documentation, rollback procedures. Not a notebook — an engineering system that runs when you're not watching.
Adoption as explicit work
Workflow integration, training, champions network, change communication. Runs in parallel with engineering — not as a post-launch afterthought.
You own it after we leave
Full documentation, runbooks, on-call procedures. We design for the scenario where the person who built it isn't available at 2am.
Instrumented from day one
Business metrics, model metrics, adoption metrics — all in one dashboard. The foundation for the continuous loop.
Measure & Evolve
This is not a phase — it's an operating mode. Continuous monitoring feeds back into the loop. The model that shipped is not the model that runs in 12 months.
Is the North Star moving?
The metric defined in Phase 0. Tracked against baseline, reported monthly, tied to business impact — not model accuracy.
Drift is silent and lethal
Accuracy, latency, error distribution, data drift against production baseline. Automated alerts when the model starts behaving differently.
Usage, override rates, workarounds
If override rates climb, something changed — in the model, the workflow, or the organisation. We detect it before it becomes invisible.
Every signal feeds the loop
User overrides become training data. Business metric gaps become model targets. The loop closes continuously — not at the next contract renewal.
The difference in practice.
| Dimension | Typical agency | Codesota approach |
|---|---|---|
| Model selection | Vendor demos, blog posts, familiarity | Independent benchmarks on standardised datasets |
| Risk detection | During or after build | Phase 01 — mapped before a single line of code |
| Adoption strategy | Training session at launch | Explicit parallel track from Phase 05 onwards |
| Trust architecture | Post-build, if at all | Designed in Phase 04 before engineering begins |
| Kill decision | After sunk costs, political | Pre-defined criteria in Outcome Charter, Phase 00 |
| Measurement | Project closes at deployment | Phase ∞ — continuous loop, business metrics first |
What this looks like in production.
Anonymised. Numbers are real.
Computer vision for automated defect detection in industrial inspection. Replaced manual visual review. Key decision from Risk Architecture: manual override is always available and every override is logged as a training signal.
LLM-powered adverse media and PEP screening. The Cost of Wrong analysis (Phase 00) determined that false negatives carried regulatory risk — so the model was tuned conservatively, with explainability designed for compliance officer review.
We work through your North Star Metric, decision rights, and what "done" actually means.
We map your top 5 riskiest assumptions. The ones that would kill the project if you discovered them in month four.
If relevant, we pull current benchmark data from Codesota and give you a ranked shortlist — before you have spent anything.
A 2-page document you keep regardless of what happens next. Useful whether you work with us or not.