Proof of Concept AI: where AI pilots break
An AI pilot usually starts with whatever data export happens to be at hand: the team builds a demo and shows a metric that inspires. In production rollout, quality drops to random guessing, but the decision has already been made and work has been assigned.
The reason is not the algorithm.
Hypothesis validation on data is replaced by validation on a convenient sample: historical labels reflect the operator's decision rather than the actual outcome, rare classes are almost absent, and features collected after the decision moment pull the answer from the future. Accuracy on such a sample predicts nothing.
The real bottleneck of the project is the data pipeline. You need rights to primary records, a reconstructed event timeline, a unified entity registry, a fixed labeling procedure, and the ability to roll back to a previous dataset version. Without this, quality is discussed in words.
In Proof of Concept AI, the metric and the threshold at which the solution goes into operation are fixed before the first build. The holdout set is split by time rather than shuffled: we test on a period the model has not seen.
Response time and the cost of processing one record are calculated separately — otherwise the solution will not survive the load.
An AI pilot is needed to remove these risks before the production environment. We break down how this is addressed.
Measurable effect of an AI prototype in the product
A prototype makes sense not as a showcase of the model's capabilities but as a measuring instrument. It is built on the customer's real data and answers one question: does the model produce a result sufficient to rely on it in the product.
The pilot's effect is calculated before full-scale development starts — the decision is made based on numbers, not the impression from a demo.
We check the quality of the model's answers on a labeled sample: the share of correct ones, the share of "insufficient data" refusals instead of plausible fabrication, and stability across repeated runs on the same inputs.
This separates tasks where the model already works from those that need a different approach — fine-tuning or stricter verification of the answer against the source. We document the limits of applicability in writing so they do not surface during implementation.
The eliminated class of failures is the other half of the result. We run the prototype under load close to production: we look at response time, resource intensity of processing one request, and behavior under peak traffic.
A queue for long requests, caching of repeated ones, predictable degradation instead of failure — these solutions are built into the prototype and transferred to the product without rework.
The hypothesis validation timeline shrinks from quarters to a few weeks: the team does not build integrations "for the future" but tests the riskiest part.
The output is a working prototype on your data, a report with metrics, and a list of scenarios where the solution is applicable. After that, the discussion about development becomes specific: what we scale and what we consciously abandon.
Pilot formats: from data review to a pilot system
Proof of Concept means different things in different teams — from reviewing data exports to an almost finished system. We distinguish three formats: they differ not in the scope of work but in how much real functionality appears in the system.
Data review and problem statement are done on real data exports: we check completeness, labeling, and discrepancies between sources, and formulate the metric by which we will measure the result.
A prototype in a limited environment is an already working model and code, but on an isolated sample, without external load or connection to internal services.
| Format | When it fits | Limitations |
|---|---|---|
| Data review and problem statement | Data quality is unknown, success criterion is not formulated | There is no working system; the effect is estimated by calculation on historical data |
| Prototype in a limited environment | Need to confirm that the approach delivers the required accuracy on your data specifically | Closed sample, no real users, response and peak loads not tested |
| Pilot system connected to internal services | The approach is confirmed, time to test it in a working environment | Requires access and a test bench, coordination with information security; longer timelines and requires support |
The formats connect to each other: each next one relies on the artifacts of the previous — a labeled set, a fixed metric, an integration scheme — so the transition does not start from scratch.
The choice is dictated not by team size but by the state of the data and the cost of error: if customer access or critical operations depend on the solution, it is reasonable to expand the limited environment to a pilot system before putting it into operation.
How we run the project: stages from hypothesis to handover
The work follows the same order regardless of domain: data review and problem statement, solution design, pipeline assembly, validation on a holdout set, demonstration, and handover.
Each stage relies on the result of the previous one, so the prototype does not drift away from the original hypothesis.
- Data review and problem statement. We look at what is available: volume, sources, update frequency, where gaps are and how they appear. We fix the metric by which we measure the result and agree in advance on what quality level we consider sufficient to move to the next step.
- Solution design. We describe how data travels from source to answer, which components are responsible for this, and where the boundaries of their responsibility are. The diagram immediately shows bottlenecks: heavy preprocessing, lack of labeling, external dependencies.
- Pipeline assembly. Model training and inference are reproduced with one command on a clean environment: versions of data, parameters, and artifacts are fixed, and the result is reproducible by any engineer on the team.
- Validation on a holdout set. Part of the data is not involved in either training or parameter tuning — only this way can you see real quality rather than overfitting. We analyze errors by class and see which of them are critical for the business.
- Comparison with the baseline. The current process or manual processing serves as the reference point: the difference shows the gain the solution provides and whether it is worth scaling.
- Demonstration. We run the solution on the customer's data, show scenarios that matter to the team, and honestly state the limitations.
- Handover to the team. We provide the code, configurations, launch instructions, solution design, and experiment log. The customer's engineers reproduce the result without our involvement — this is the criterion for a completed stage.
This order makes the assessment of the next step specific: you can see which data to strengthen and what to change in the design.
What remains with you after the pilot: artifacts and procedures
A pilot is justified only when it leaves the customer's team with working materials, not a presentation with conclusions. That is why we close each stage with a handover: code, environment, and data remain in your environment, not ours, and are launched without our involvement. You receive a set:
- Working solution design — architecture with data flows, integration points, and a list of external dependencies, so scaling does not start with reverse engineering.
- Code and reproducible environment — deployment of the test bench from scratch according to instructions, together with pinned dependencies and configurations.
- Data access rules — role matrix, separation of sensitive fields, access logging, and the procedure for connecting new sources.
- Set of validation examples — a reference sample with expected answers, on which any run produces comparable quality metrics.
- Solution documentation — limits of model applicability, feature set, known limitations, and conditions under which quality drops.
- Operations procedure — monitoring of data and quality drift, alert thresholds, retraining procedure, and rollback plan to the previous build.
Each item is designed to be used by an engineer who did not work on the pilot. If your team or another contractor takes over support, the context is restored from documents, not from chat history.
Case study: how a pilot on real data removed a blocker
Customer requests flowed into a common queue: an operator manually sorted emails and requests from 1C, determined the topic, found the contract, and assembled a response from templates.
Processing one request took an average of about 25 minutes; on peak days the queue stretched to two days, and the share of repeat requests for the same reason held at 18% — the client did not receive a clear answer on the first try.
The pilot was built on historical data: 40 thousand closed requests with real operator decisions. The sample was labeled by topic and action type, and the holdout part that did not participate in tuning was set aside separately.
We checked three things: accuracy of topic identification, correctness of extracting details from documents, and whether response preparation fits within the target request processing time.
What changed after validation
On the holdout set, the topic was correctly identified in 92% of cases, and contract details were extracted without edits in 87%.
Average request processing time dropped from 25 minutes to 6: the operator received a draft response with references to contract clauses and already confirmed data. The share of repeat requests for the same reason decreased from 18% to 7%.
We separately recorded the limits of applicability: on rare topics and poorly readable scans, quality dropped, so in the pilot environment we kept a rule — when uncertain, the system does not answer but passes the request to an operator with a note. This same rule formed the basis for load calculation after launch.
What to do if the model is already deployed in production
A deployed model is not the finish line but the start of operation. Over time, quality drops: the request flow changes, new segments appear, some fields arrive empty.
Retraining "by eye" does not help here — without an audit of the current solution, it masks the symptom and in a month the degradation returns.
The audit starts with a reference slice: we fix the sample on which the solution showed the claimed result and run it again. If the metrics hold on it but drop on live traffic, the issue is in the data, not the weights.
Next, we calculate metrics by segment (source, request type, time of day) and compare input feature distributions with the launch period. This separates data drift from a change in the task logic itself — these are two different diagnoses and two different treatments.
It is convenient to manage validation labeling in Label Studio: disputed cases from production go into the same stream as the historical sample, and we see not only that "it got worse" but also on which specific examples.
Code reuse here is the main source of time savings. Validation schemes, preprocessing, inference wrappers, and the regression set remain; only what is proven broken changes.
Improving an existing model — fine-tuning on fresh data, relabeling problematic segments, replacing a separate pipeline block — is faster than building a solution from scratch and preserves the experiment history, which shows which hypotheses have already been tested.
After the audit, you have a report with before-and-after metrics, a bottleneck map, and a regression set that catches degradation before the user notices it.
Typical technical concerns and limitations
Before starting a pilot, the customer checks three things: whether there is enough data, whether it can be shared without risk to the closed environment, and how to make sure the result is not fitted to the demo. These concerns define the pilot's boundaries — and what will remain with you after it.
Volume is determined not by the size of the export but by how many examples are needed for the metric to stop fluctuating: for a frequent class, a few thousand labeled cases are usually enough.
If there is less data, it is more honest to narrow the task to one scenario than to spread the model across all of them at once.
Will our data volume be enough?
We count not rows in a table but labeled examples per class and their diversity. If rare cases are few, this is recorded as a pilot limitation: we test the model on frequent scenarios, and rare ones go into the data collection plan.
How to transfer anonymized data?
Masking is done on your side or according to our rules: direct identifiers are replaced, and the mapping remains with you. Only the features needed by the model go to our side; for closed environments, we work without exporting data outside.
Is access to closed systems needed?
Full access is not required: scheduled exports or a read-only replica are enough. Access is granted for the duration of the pilot and revoked afterward — we agree on the procedure before work starts.
How reliable are the results, and what happens to the prototype next?
We measure quality on a holdout set the model has not seen and compare it with the current process, not with an ideal. The report includes metrics, error analysis, and limits of applicability.
The prototype, labeling, and configurations remain with you: they can be developed into a production version or kept as a validation bench.
Let's discuss the task and come back with a pilot plan
For the timeline estimate to be a calculation rather than a declaration, we need a minimum of inputs: an anonymized data sample for a representative period, a description of the process you want to remove from people, and the criterion by which you will consider the pilot successful.
The more specific this criterion is, the more accurately we will divide the work into stages and the lower the chance that the prototype turns out to be "interesting but useless".
- Inputs from you: data sample, description of the current process and its bottlenecks, success criterion in measurable form, access to source systems.
- First data review: we look at completeness, labeling, duplicates, discrepancies in formats and units — usually takes a few days and does not require your engineers to participate.
- Data conclusion: what is usable immediately, what will need cleaning, where data is objectively insufficient, and how to close that gap.
- Pilot plan: hypothesis, metric for testing it, scope of work, and hard boundaries — what is deliberately not included in the pilot.
- Timeline estimate: separately for data review, prototype assembly, and validation of the result on your side, so the stages can be read independently.
- Deliverable format: a working prototype, launch instructions, a report with metric measurements, and a list of what prevents moving to a production version.
- Stop condition: we fix in advance the criterion by which we consider the result negative and what remains with you in that case.
Describe the task in free form — we will come back with a pilot plan, timelines by stage, and a clear first step on the data.
