AI proof of concept that gives you a go or no-go answer before you commit.

• 01

  LLM and RAG PoCs
  Test whether a large language model approach works on your specific documents and data before committing to full RAG pipeline infrastructure. We compare leading models, GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, on accuracy, hallucination rate validated against your experts, latency, and cost per 1,000 queries, and model the cost at your expected volume so commercial viability is clear before any build decision.
• 02

  Computer vision PoCs
  Train a computer vision model on a stratified sample of your labelled images and measure performance against your defined accuracy requirements. The PoC audits dataset quality, trains a transfer-learning baseline with YOLOv8 or EfficientNet, reports per-class precision, recall, and F1, and projects whether more labelling investment will close the gap to target accuracy.
• 03

  Predictive analytics PoCs
  Test whether your historical data contains enough signal to predict the outcome you care about at an accuracy you can act on. A LightGBM baseline trains on 12 to 24 months of data with time-sorted splits, feature importance confirms the driving variables are available at prediction time, and results are benchmarked against your current approach and translated into business value.
• 04

  AI agent PoCs
  Prototype an AI agent that executes a multi-step workflow, document processing, data enrichment, research, or decision routing, to measure real reliability before investing in production infrastructure. Built on LangGraph and run against 30 to 50 real workflow inputs, evaluation covers task completion, step accuracy, failure modes, and latency, giving an evidence-based answer on where the agent can run autonomously.
• 05

  Data assessment and feasibility
  Before writing a line of model code, assess whether your data can support the AI approach you're considering, because a PoC on inadequate data only reveals the data gap. The assessment covers completeness, volume against accuracy requirements, label quality, class distribution, and temporal coverage, producing a written feasibility report with a realistic accuracy range and a clear recommendation to proceed or collect data first.
• 06

  Multi-approach comparison
  Systematic comparison of two to three AI approaches, a fine-tuned classifier, a zero-shot LLM, and RAG, against the same business problem, evaluated on the same test set with the same success criteria, so the architecture decision is evidence-based rather than a technology debate. Each approach runs on 100 to 200 real test cases with cost per 1,000 inferences and maintenance burden assessed, ending in a clear recommendation with reasoning and caveat cases.

• 01

  Success criteria first
  Before a single line of model code is written, we document and get sign-off on the metrics that define success: the exact accuracy threshold, whether false positives or false negatives cost you more, the held-out test dataset of 100-200 real examples, and the improvement over your current process that justifies the investment. That specification becomes the contract. At PoC conclusion we measure against it and deliver a written go or no-go verdict either way.
• 02

  Real data, not synthetic
  AI PoCs tested on clean synthetic data overstate production performance, often by a wide margin. We work exclusively with your actual data: skewed scans, missing fields, duplicate records, and lighting variation included, so the accuracy we report is the accuracy you will see in production. Where data contains PII, we anonymise while preserving the structural properties that determine model performance. A 4-week PoC typically needs 100-200 labelled test examples plus 500-2,000 for training.
• 03

  Honest go/no-go verdict
  The PoC conclusion report gives a clear written verdict: go, conditional go with a stated scope limitation, or no-go. No-go is a successful outcome. We have called it when training data was insufficient, when inference cost at volume exceeded the business value, and when the available features simply could not predict the outcome. Every no-go includes the quantified gap, root cause, and costed options for closing it, including a data collection plan where that is the fix.
• 04

  Full development estimate included
  A PoC that concludes go or conditional go includes a complete full-development specification: recommended architecture, data pipeline design, integration spec, monitoring approach, a sprint-by-sprint plan, a fixed cost estimate, and a delivery timeline. You move from PoC to full development without a second round of scoping. If you proceed with us, the PoC cost is credited toward the full engagement, and you already know how we work before the bigger commitment.

1. Week 1
   01

   Discover and scope

   We map your business problem, data state, and AI hypothesis. You leave week 1 with a written PoC specification: the exact success criteria, test dataset definition, and a fixed-price quote. No development starts without your sign-off.

2. Weeks 1-2
   02

   Data assessment and preparation

   We audit your data for volume, quality, and distribution. If the data is insufficient, we tell you before spending budget on a model. Data gaps are documented with a remediation estimate so you have a clear next step.

3. Weeks 2-6
   03

   Build, test, and measure

   Model training, evaluation, and iteration against the agreed test set. We report performance weekly, not at the end. If we hit the success threshold early, we document it and move to the verdict report.

4. Week 6-8
   04

   Go/no-go verdict and next steps

   A written verdict measured against the agreed success criteria. Go includes a full development specification, architecture document, and fixed-price estimate. No-go includes the gap analysis, root cause, and options to address it.

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