Knowledge is scattered
Policies, product notes and operational knowledge live in documents, chats and people’s heads, so answers depend on who is asked.
SERVICES / AI INTEGRATION
AI integration for business systems
We integrate AI where it can be evaluated, explained and operated — not where it only looks impressive in a demo. The work starts with the data, the user decision and the evidence needed to trust the answer.
Business problems we solve
A pilot that cannot ship
The demo answers a few questions well, but there is no evaluation set, access control, cost model or plan for wrong answers.
Automation without accountability
A workflow is automated before anyone defines who reviews exceptions, what is logged and how a bad output is corrected.
What we build
Retrieval and knowledge assistants
RAG systems that search approved sources, cite passages and respect the permissions of the person asking.
Workflow automation
Assistive steps for drafting, classification, extraction and review, with a human decision where the risk requires it.
Model integration
Connecting models to existing services, data and interfaces without making the product dependent on one vendor.
Evaluation and monitoring
A test set, acceptance criteria, citations, cost and latency tracking, and a process for reviewing failures.
What you receive
- A source and permission map for the data the system may use.
- An architecture and data-flow design with failure modes.
- An evaluation set and acceptance criteria agreed before launch.
- Retrieval with citations and a clear “insufficient evidence” path.
- Access control, audit logging and retention rules.
- Monitoring, cost visibility and a handover runbook.
A clear path from idea to delivery.
Discover
Understand your users, constraints and goals.
Define
Agree the scope, approach and success criteria.
Build & verify
Develop in increments with visible progress and testing.
Deliver & evolve
Release, document and plan what comes next.
Technical capabilities
Retrieval design
Chunking, metadata and hybrid search shaped around the documents and questions that matter.
Evaluation
Questions with known answers, negative cases and human review to measure whether the system is useful.
Access control
Filtering by source, role and tenant before content is placed in a model’s context.
Operations
Latency, cost, failure handling and index refresh planned as part of the system, not after launch.
How we work together
Evaluation first
Define the decision, the sources and the evidence before choosing a model or interface.
Pilot to production
Start with one workflow and a measured result, then expand only where the evidence supports it.
Human review by design
Exceptions and high-risk answers stay with a person, with the context needed to review them.
Frequently asked questions
Do you train foundation models?
We integrate and evaluate existing models. Fine-tuning is considered only when the problem and evidence justify it; most business systems need better retrieval, permissions and evaluation first.
Can the system read our internal documents?
Only sources you approve. We map permissions first and filter results before they reach the model, so an answer cannot expose a document the user is not allowed to see.
How do you measure whether the answers are correct?
With an evaluation set, source citations and human review. We report what the system can and cannot answer; we do not promise universal accuracy.
What about cost and latency?
They depend on the model, context size and traffic. We measure both in the pilot and set a budget and fallback path before production.
What happens when the AI is wrong?
The system should show its source, allow a correction path and log the failure. High-risk decisions remain with a human reviewer.
LET’S MAKE SOMETHING USEFUL
What are youworking on?
Tell us about your idea, the challenge and what success looks like. We’ll start with a thoughtful conversation about scope and fit.
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