AI PoC Development

Test whether a large language model approach works on your specific documents and data before committing to full RAG pipeline infrastructure and integration development. PoC scope for RAG: document ingestion pipeline (PDF parsing with pdfminer or PyMuPDF, chunking strategy selection, fixed 512-token with overlap vs semantic chunking via sentence boundary detection), embedding model selection (OpenAI text-embedding-3-large vs Cohere embed-v3 vs open-source sentence-transformers benchmarked on your specific query/document pairs), vector store selection (Pinecone, pgvector, Chroma, or Weaviate tested with your document set), retrieval quality measured using MRR@5 (Mean Reciprocal Rank for the top 5 retrieved chunks) and recall@5 against a manually curated test set of 50--100 representative queries and their correct source documents. LLM selection and prompt engineering: GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro compared on accuracy, hallucination rate (validated against ground truth from your subject matter experts), response latency (p50/p99 per query), and cost per 1,000 queries at your expected volume. Hallucination measurement: responses tested against the retrieved context (LLM-as-judge with a critique prompt) and against ground truth answers for the test query set; hallucination rate reported as a percentage of responses containing a claim not supported by the retrieved context. Cost modelling: total inference cost per query at your expected daily and monthly volume across all candidate models, so the commercial viability of the approach is clear before the build decision.

Train a computer vision model on a stratified sample of your labelled images and measure performance against your defined accuracy requirements, establishing whether your current labelled dataset supports the accuracy you need or whether a data collection programme is a prerequisite to a production build. PoC methodology for vision tasks: dataset audit (total labelled images, class balance analysis, annotation quality review on a 10% sample, image quality assessment, resolution, blur, lighting variation, occlusion frequency); baseline model training using transfer learning from a pre-trained backbone (YOLOv8s for detection tasks, EfficientNet-B0 for classification) on your labelled images with a 70/15/15 train/val/test split; evaluation on the held-out test set with per-class precision, recall, F1, and confusion matrix; identification of the hard cases (false negatives in each defect class, confusion between similar classes) with visual inspection and root cause analysis (insufficient examples of the confused classes, annotation inconsistency, or genuinely ambiguous visual similarity). Sample data requirements for the PoC: minimum 100--200 labelled images per class to establish feasibility; 500+ images per class to establish a meaningful upper bound on production accuracy. Accuracy ceiling estimation: learning curve analysis (model performance at 20%, 40%, 60%, 80% of the training data) to project whether additional labelling investment will close the gap to the target accuracy. Clear output: the PoC concludes with a precision/recall/F1 per class on the test set, a go/no-go recommendation against the agreed accuracy threshold, and an estimate of the labelling investment required to reach the target if the current dataset is insufficient.

Test whether your historical data contains sufficient signal to predict the outcome you care about at the accuracy level that would make the prediction actionable for your business decision. PoC for predictive analytics: data audit (12--24 months of historical data with the target outcome and candidate feature variables reviewed for completeness, consistency, and temporal coverage); baseline model training using gradient boosting (LightGBM as the default first approach for tabular data) with cross-validated evaluation on time-sorted splits (not random splits, which overstate performance when temporal patterns matter); feature importance analysis identifying the 5--10 variables that drive most of the predictive signal (and surfacing whether those variables are available at prediction time in production, a model that needs next month's revenue to predict next month's churn is not deployable). Evaluation metrics tied to the business decision: AUC-ROC for ranking tasks (churn risk scoring, lead scoring) where a ranked list is the output; F1 and precision/recall curve at the operating threshold for binary classification; WAPE/MASE for demand forecasting; MAE for regression. Comparison against the current baseline: performance benchmarked against the current approach (rule-based scoring, gut instinct, or no prediction) so the improvement is quantified, not just described as "better." Business value modelling: given the model's precision at the operating threshold, how many true positives would be actionable per month at your transaction volume, and what is the expected value of acting on them vs the cost of false positive rate? The PoC answers whether the ML approach produces enough incremental value over your current process to justify the investment.

Prototype an AI agent that executes a multi-step workflow, document processing, data enrichment, automated research, or decision routing, to measure the actual reliability, accuracy, and error rate before investing in the production infrastructure, monitoring, and human-escalation workflows that a production agent requires. PoC methodology for AI agents: define the workflow as a sequence of steps with a clear expected output per step and a success criterion for the complete workflow; implement the agent using LangChain/LangGraph or a thin custom ReAct loop (the choice informed by the workflow complexity, LangGraph for branching/looping workflows, a custom loop for strictly linear workflows); define the tool set (web search, database query, file read, API call, structured data extraction) with typed interfaces and error handling; run the agent against 30--50 representative real-world workflow inputs from your actual data. Evaluation dimensions: task completion rate (percentage of inputs where the agent produces a complete, valid output without error or stall); step accuracy (percentage of individual tool calls that return the expected result, distinguishing between the agent reasoning correctly but the tool failing vs the agent making an incorrect tool call); error analysis (classification of failure modes: tool call failure, reasoning error, loop/stall, hallucinated intermediate step); latency measurement (total elapsed time per workflow from input to output, and the distribution across 50 runs). Reliability vs autonomy trade-off: the PoC establishes where the agent is reliable enough to be fully autonomous, where it needs human review for specific step types, and where the overall task is not suitable for autonomous agent execution with your current data and tooling, giving you an evidence-based architecture decision for the full build.

Before writing a line of model code, assess whether your data is sufficient for the AI approach you're considering, because starting a PoC with data that cannot support the accuracy you need produces a failure that reveals only the data gap, not whether the AI approach is viable when data is adequate. Data assessment deliverables: completeness report (percentage of records with each candidate feature field populated; missing value analysis per field and the pattern of missing data, missing at random vs systematic gaps for certain time periods, customer segments, or product categories); volume assessment against accuracy requirements (for classification: is the labelled dataset size per class in the range that typically achieves the target accuracy for this task complexity? Based on published benchmark studies and our experience with similar tasks); label quality audit (sample 10% of labelled records and assess annotation consistency, do two annotators agree on the label for ambiguous cases?); distribution analysis (is the class distribution in the labelled dataset representative of the production distribution, or is the majority class 95% of records, making recall for the minority class unachievable at the data volume you have?); temporal coverage check (for time-series and forecasting tasks: does the historical data cover enough full seasonal cycles to learn seasonal patterns? 12 months is the minimum; 24 months is preferred for reliable seasonal forecasting). Output: a written data feasibility report with a realistic accuracy range estimate given your current data state, a labelling investment estimate to reach the target, and a clear recommendation on whether to proceed to PoC with current data or invest in data collection first.

Systematic comparison of two to three AI approaches against the same business problem, evaluated on the same test set with the same success criteria so the comparison is valid and the architectural decision is evidence-based rather than a debate between proponents of different technologies. Common comparison scenarios: fine-tuned BERT classifier vs zero-shot LLM classification vs RAG-based classification for document routing (comparing per-class F1, inference cost at volume, and maintenance burden when new categories are added); LLM prompt engineering vs fine-tuned GPT-3.5/Llama 3 vs RAG over domain documents for domain-specific Q&A (comparing accuracy, hallucination rate, and cost-per-query); YOLOv8 object detection vs classification-based approach vs LLM vision model (GPT-4V, Claude 3 Vision) for document layout analysis (comparing accuracy, inference speed, and deployment complexity). Comparison evaluation framework: each approach evaluated on the same 100--200 test cases from your real data; metrics calculated per approach on the same evaluation script so results are directly comparable; cost per 1,000 inferences calculated for each approach at your expected production volume; operational complexity assessed (what does ongoing maintenance and model update look like for each approach, does it require retraining on new labelled data, or is it a prompt change?). Recommendation: the PoC concludes with a clear recommendation of which approach to use in production, the reasoning behind the recommendation (not just "it performed best on the test set" but why the performance difference matters at your volume and what the maintenance trade-off is), and the caveat cases where the runner-up might be preferable (cost-constrained deployment, privacy-constrained environment requiring local inference).

Before a single line of model code is written, we document and get sign-off on the specific metrics that define success for your use case: the exact accuracy threshold (e.g., 92% field extraction accuracy on invoice amounts, 85% F1 for churn prediction, WAPE below 15% for monthly demand forecasting), the acceptable error rate and whether false positives or false negatives are costlier for your business decision, the test dataset (typically 100--200 real examples held out from training, drawn from your production data with a representative distribution of edge cases), and the minimum business performance requirement that makes the AI system commercially viable (what improvement over the current manual or rule-based approach justifies the development investment?). These criteria are written into the PoC specification document that both parties sign before development starts. The specification becomes the contract: at PoC conclusion, we measure the results against the specification and provide a verdict in writing. If the system meets the threshold, the verdict is "go" with a full development specification. If it does not, the verdict is "no-go" with the specific gap, the root cause analysis, and what would need to change (more labelled data, different architecture, different model size) to close it. You get a clear, actionable answer regardless of which way it goes.

AI PoCs that are tested on clean, hand-curated synthetic data systematically overstate production performance, sometimes by 10--20 percentage points, because synthetic data lacks the noise, formatting variation, edge cases, and data quality issues present in real production inputs. We work exclusively with your actual data: your real invoices with the skewed scans and inconsistent vendor formatting; your real customer records with the missing fields, duplicate entries, and historical migrations that left gaps; your real images with the lighting variation, motion blur, and partial occlusions from your actual operational environment. If your data is messy, and most production data is, we test on that messy data and report the accuracy you will actually see in production, not the accuracy achievable on a curated sample. Data anonymisation for PoC testing: where your actual data contains PII or commercially sensitive information, we work with you to anonymise or pseudonymise the dataset for PoC testing purposes (customer names replaced with synthetic names, contract values replaced with anonymised amounts) while preserving the structural properties (field distributions, missing value patterns, format variation) that determine model performance. Minimum viable data sample: for a 4-week PoC, we typically need a test set of 100--200 labelled examples and a training/development set of 500--2,000 examples; if less is available, the PoC scope is adjusted to establish what accuracy is achievable with the data you have today and whether data collection is a prerequisite to a viable build.

The PoC conclusion report gives you a clear, written verdict in three possible outcomes: go (the approach meets the defined success criteria at the required accuracy and cost, proceed to full development), conditional go (the approach meets criteria on well-represented cases but fails on a specific edge case category, proceeding is viable with the stated scope limitation or data gap addressed), or no-go (the approach does not meet criteria and the gap cannot be closed without a material change, more labelled data, a different model architecture, or a revised accuracy target that reflects what is actually achievable with your data). No-go is a successful outcome. We have called no-go on PoCs where the training data volume was insufficient to reach the target accuracy, where the inference cost at production volume would have exceeded the business value of the predictions, and where the model's accuracy ceiling was fundamentally limited by the information content of the available data (the features available to the model didn't predict the outcome with enough reliability to be useful). Each no-go verdict includes the specific quantified gap (e.g., "current model achieves 73% F1 vs 85% target; learning curve analysis suggests 5,000 additional labelled examples would close this gap") and the options for addressing it with cost and timeline estimates for each option. If the verdict is no-go due to data insufficiency, we provide a data collection and labelling plan so you can revisit the PoC in 3--6 months with adequate data rather than writing off the concept.

A PoC that concludes with a go or conditional go recommendation includes a complete full-development specification as a deliverable: the recommended architecture (model, serving infrastructure, integration points), the data pipeline design (how training data is maintained and the model retrained as new data accumulates), the integration specification (APIs, data schemas, and the downstream systems the model output feeds into), the monitoring and drift detection approach (how accuracy degradation will be detected after launch), a sprint-by-sprint development plan with deliverables per sprint, a fixed cost estimate, and a delivery timeline. You move from PoC conclusion to full development kickoff without a second round of scoping, the specification produced by the PoC is the architecture document and the project plan. If you proceed to full development with us, the PoC cost is applied as a credit toward the full development engagement. The PoC also establishes the working relationship: you have seen how we communicate, how we handle a finding that doesn't go the way you hoped, and what a typical week of progress looks like. The full development engagement starts with that context established rather than the uncertainty of a new team relationship.