Intelligence
AI Development Services for Real Business Use Cases
Useful AI is an engineering problem: grounded context, measured output, sensible fallbacks and a clear place in the workflow.
“Q3 net recurring margin increased 24.8% due to automated cloud routing.”
Overview
Where this fits
A demo can be built in an afternoon. A dependable AI feature needs retrieval over your own data, evaluation you can run on every change, and a defined behaviour when the model is unsure.
We design AI systems that sit inside existing products — drafting, summarising, extracting, classifying, matching — with cost, latency and accuracy treated as first-class requirements.
What we build
Delivery within ai solutions & generative ai
Retrieval-grounded assistants
Systems that answer from your documents, records and policies with citations.
Document intelligence
Extraction, classification and validation across contracts, invoices and forms.
Generative interfaces
Drafting, rewriting and content generation embedded where the work already happens.
Evaluation harnesses
Test sets, scoring and regression checks so quality is measured, not assumed.
Problems we solve
What usually brings clients here
- 01Impressive prototypes that fail on real, messy company data.
- 02Model output nobody can verify or trace to a source.
- 03Unpredictable token cost with no visibility per feature.
- 04No plan for what happens when the model is wrong.
Key capabilities
What is included
- Retrieval-augmented generation
- Embeddings and vector search
- Structured output and function calling
- Prompt and context engineering
- Evaluation, scoring and regression testing
- Guardrails, redaction and audit logging
Models
Leading hosted and open-weight LLMs, chosen per task
Retrieval
Vector databases, hybrid search, re-ranking
Safety
Permission-aware retrieval, redaction, audit trails
Operations
Caching, routing, cost and latency monitoring
Use cases
Where it makes the biggest difference
Internal knowledge answers
Staff asking questions of policies and documentation and receiving cited answers.
Back-office extraction
Turning inbound documents into validated structured records.
Content acceleration
Drafting within brand and compliance constraints, with human approval.
Our approach
How the engagement runs
01
Qualify
Confirm the task is a fit for AI and define what a correct answer looks like.
02
Ground
Prepare data, chunking, retrieval and permissions.
03
Evaluate
Build a scored test set before scaling the feature.
04
Operate
Monitor quality, cost and drift in production.
FAQs
Questions we are asked
Does our data train a public model?
No. We use configurations and providers where your data is not used for training, and we document the data path.
How do you measure AI quality?
With a labelled evaluation set and scoring that runs on every prompt, model or retrieval change.
Can this run on our own infrastructure?
Yes, where open-weight models meet the quality bar we can deploy within your environment.
Related
Capabilities that usually travel with this
Planning ai solutions & generative ai?
Let's scope it properly.
Tell us the problem, the constraints and the deadline. We will come back with an approach, not a brochure.