Our AI Solutions — From Strategy to Production
Most AI programs don’t fail at the model. They fail at everything around it — an unclear business case, data that isn’t ready, a prototype nobody can deploy, a system nobody can explain to a regulator. We work across the whole chain: the strategy, the build, the deployment, and keeping it running afterwards.
What We Build
AI Strategy & Advisory — Where AI will actually pay back, and where it won’t. Roadmap, ROI modeling and readiness assessment tied to your business goals.
Agentic AI & Multi-Agent Systems — Agents that execute multi-step work against your own systems and policies, bringing a person the decisions that need judgment.
Generative AI & Enterprise RAG — Answers drawn from your own documents, cited back to the source. Document intelligence, intelligent search and conversational interfaces.
Machine Learning Engineering — Predictive models for forecasting, risk scoring and anomaly detection, built to explain their decisions as well as make them.
Computer Vision & Document Intelligence — Detection, classification and extraction from images, scans and video, each result carrying a confidence score.
Data Engineering — The ingestion, transformation and feature pipelines everything above depends on.
MLOps, LLMOps & AgenticOps — Deployment, monitoring, evaluation and governance, so systems keep working after launch.
Proof of Concept & Rapid Prototyping — A working demo against your real data before anyone commits to a full build.
Evaluation & Governance
A system you can’t measure is a system you can’t explain to a regulator. We measure quality and keep a person in control before anything reaches production.
Performance — Better answers from the same documents. We rerank retrieved evidence and compress context before generation, and use multimodal retrieval that reads text, tables and images in complex PDFs.
AI Governance — Quality you can measure, not assume. We score retrieval and answers with RAGAS and DeepEval, and check summary faithfulness with LLM-as-a-Judge and BERTScore, with per-example drill-down to see exactly where a response went wrong.
How We Work
- Tie every initiative to a business measure before any code is written
- Choose the right tool for the problem — classical ML, generative AI or agents — rather than the fashionable one
- Build a proof of concept first, and validate it against real data
- Ship with CI/CD, monitoring and guardrails built in, not bolted on
- Keep improving through evaluation, feedback loops and drift detection
From Prototype to Production
- Data ingestion, validation and feature engineering
- Model training, evaluation and experiment tracking
- Fine-tuning, prompt engineering and RAG pipeline construction
- Continuous integration, deployment and A/B testing
- Monitoring, alerting and drift detection
- Feedback loops that improve the system over time
Built primarily on Microsoft Azure, with experience across AWS, Google Cloud and Oracle Cloud Infrastructure.
