Custom AI built around your data and your systems

We connect a very capable general-purpose brain to your specific information, and put guard rails around it.

The clever part — the model that understands language or images — already exists and is very good. What it does not have is any knowledge of your business. So the job is teaching it, safely, about your documents, your products, your customers and your rules.

We start small and real. Give us fifty of your actual invoices, or a hundred of your actual support questions, and we build something that handles those. Then we test it against cases it has not seen, count how often it is right, and show you the score honestly. If the score is not good enough to be useful, you find that out in week two for a small amount of money, not in month six for a large one.

Once it works, we wire it into your systems, add the checks that stop it doing anything silly, and put a person in the loop wherever a mistake would be expensive. Then we write it all down and hand you the keys — the code, the accounts, the documentation.

Afterwards, things change: providers update models, your products change, volumes grow. That is why we build a test set from your real cases. When anything changes, we can prove in minutes whether the system still does its job, instead of finding out from a customer.

Foundation models behind an application layer we own end to end: typed extraction, hybrid retrieval, deterministic validation, evaluation harness, observability.

Model strategy. Default to hosted frontier models via API for quality and operational simplicity; open-weight models on self-managed infrastructure where data residency, cost at volume or latency demands it. We benchmark two or three candidates against your own task before committing, and we keep the provider swappable behind an internal interface so a deprecation is a config change and a regression run, not a rewrite.

Retrieval. Document parsing with layout awareness, semantic chunking with overlap tuned per corpus, embeddings in pgvector or a managed index, hybrid BM25 plus dense retrieval, cross-encoder re-ranking, and metadata filters carrying row-level permissions so retrieval itself enforces access control rather than the prompt asking nicely.

Extraction. Structured outputs constrained to a JSON schema, with per-field confidence and deterministic post-validators: arithmetic, checksums, referential integrity against master data, date plausibility. Anything failing validation routes to human review with the original document rendered alongside the proposed values.

Forecasting. Gradient-boosted trees or classical statistical models first — they are usually more accurate, far cheaper and much easier to explain than a deep model on the data volumes most Kenyan businesses actually have. Backtested on rolling origin with honest error bars.

Evaluation. A golden set from real historical cases with expected outputs; task-appropriate metrics; LLM-as-judge only where it correlates with human scoring on a validated sample. Runs in CI on every prompt, model or retrieval change.

Delivery. Your repository, your cloud account, infrastructure as code, secrets in a managed vault, structured logging with trace IDs across the whole call chain, dashboards and alerting, and a documented rollback. Handover includes an architecture decision record explaining why each significant choice was made — including the compromises.