RAG and knowledge systems
Create traceable ingestion, retrieval, permissions and citations around private or changing information.
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INDUSTRY / AI
AI software engineering
Build grounded AI products and automation with evaluation, permission controls and observable production behaviour.
01 / OPERATING REALITY
What matters in this industry.
The business value of AI depends on more than model quality. Source evidence, workflow fit, privacy, evaluation, latency and the cost of human review determine whether a prototype becomes dependable software.
02 / WHAT WE DESIGN FOR
Connected capabilities for AI teams.
AI agents and automation
Bound tool access, state, approvals and escalation so probabilistic decisions cannot create uncontrolled impact.
Evaluation and operations
Measure task success, groundedness, latency and cost with regression tests and production traces.
03 / PRACTICAL SCENARIOAI / DELIVERY CONTEXT
A support team needs answers it can verify
A representative service business has product documentation across several systems. Agents lose time searching, while a chatbot prototype produces confident answers without reliable citations.
We would establish a question set, normalise approved sources, enforce user permissions during retrieval and surface passage-level citations with a clear refusal path.
The target is an assistive workflow that shortens research while keeping the human agent accountable for the customer response.
Illustrative scenario: the intended outcome is not a measured client result.
04 / PRACTICAL ANSWERS
AI engineering FAQ
What should an AI project prove first?
It should prove a measurable task improvement using representative data, along with acceptable error, privacy, latency and operating cost.
How do you reduce hallucinations in enterprise AI?
Use high-quality authorized evidence, retrieval evaluation, citations, explicit uncertainty and task-specific tests. No single prompt or model setting eliminates hallucinations.
Have a AI product or platform challenge?
Tell us what must change, what cannot fail and what your team needs to own.
Talk to an engineer