Generative AI
LLM-powered features: retrieval-augmented assistants, content drafting, summarisation and search over your documents with evaluation and guardrails.
Generative AI features grounded in your own content and evaluated before launch.
6 deliverables, a 4-stage approach and 5 related technologies.
Overview
Large language models are powerful drafting and reasoning tools, but they can state wrong things confidently. Grounding answers in your own documents and evaluating them systematically is what makes them dependable.
We build retrieval-augmented generation, structured outputs, prompt and model evaluation suites, and guardrails for privacy, tone and scope — and we compare models on your tasks rather than assuming one is best.
What we deliver
- Use-case definition and success criteria
- Retrieval pipeline over your content
- Prompt and output design
- Evaluation dataset and automated scoring
- Guardrails and logging
- Cost and latency estimates per request
Use cases
Typical situations where this service is the right fit.
Knowledge assistants
Answer staff questions from policies and manuals with citations.
Content drafting
Draft descriptions, replies or reports for human editing.
Search
Semantic search across documents and tickets.
How we work
The stages we follow. Each ends with something you can review.
- 01
Collect real questions before building
- 02
Ground answers in retrieved sources
- 03
Evaluate on a fixed test set
- 04
Monitor cost, latency and quality in production
Things to consider
Questions worth answering before you commit budget.
- Sending data to external model APIs has privacy implications; self-hosted models are an option.
- Usage-based costs need budgets and limits.
Technologies and engagement
Technologies we often use
We choose the stack with you; this is a starting point, not a requirement.
Ways to engage
- Fixed-scope project with milestones
- Dedicated team billed monthly
- Ongoing support and improvement retainer
Frequently asked questions
Related services
AI agents
Tool-using AI agents that take multi-step actions in your systems — with scoped permissions, approvals and full audit trails.
Chatbot development
Website, app and WhatsApp chatbots — rule-based or AI-powered — with knowledge grounding, human hand-off and analytics.
AI and machine learning
Classical machine learning, forecasting, classification, recommendation and computer vision — from feasibility to monitored production models.
Ready to talk about generative ai?
Share a few details and we will reply with questions, options and an honest view of effort.