Clear, owned outputs.
- Use-case assessment
- Prototype tested on real examples
- Evaluation framework and results
- Production deployment with guardrails
- Usage policy and training
Generative AI can draft, summarize, answer questions and work with language and images in ways that were impractical before. It can also be confidently wrong. We build generative AI solutions that are grounded in your content, evaluated before launch, protected by guardrails and designed so people stay in control.
The problems Generative AI solves.
Assistants, knowledge tools and copilots built with evaluation, security and oversight.
Staff use public AI tools with company data and no policy.
Pilots impress in demos but give wrong answers in real use.
It's unclear which tasks generative AI can safely support.
Costs and data handling of AI tools are hard to control.
What Generative AI covers.
Use-case design
Choosing tasks where generative AI adds value with acceptable risk.
Knowledge assistants
Answering questions from your documents and systems.
Content generation
Drafting text and images within brand and policy rules.
Workflow copilots
AI built into the tools and steps people already use.
Evaluation and guardrails
Testing accuracy, safety and behaviour on realistic cases.
Governance
Usage policies, access control, logging and cost monitoring.
Prompting, RAG or fine-tuning?
Most generative AI projects don't need a custom model. The question is how much the model needs to know about your business, and how current that knowledge must be.
| Approach | What it changes | Use it when |
|---|---|---|
| Prompt engineering | Instructions, examples and output formats given to an existing model. | The task is general and the model already knows enough. |
| Retrieval-augmented generation | The model answers from your documents, retrieved at query time. | Answers must reflect your own, changing content and cite sources. |
| Fine-tuning | The model's weights are adjusted on your examples. | You need a consistent style or format at scale that prompting can't achieve. |
| Agents and tools | The model can call APIs and take steps. | The task needs actions in other systems, not just text. |
Why it's worth it.
Time saved
Drafting and searching take minutes instead of hours.
Controlled risk
Grounding, guardrails and review reduce errors.
Safe adoption
Approved tools replace unmanaged ones.
When Generative AI is the right move.
- An internal assistant answering policy and process questions.
- Drafting first versions of proposals or reports.
- Summarizing calls, tickets or case notes.
Technologies & platforms
- Azure OpenAI
- OpenAI API
- Anthropic Claude
- AWS Bedrock
- Google Vertex AI
- LangChain
- LlamaIndex
- pgvector
How Generative AI work runs.
- 01
Scope
The task, users, content sources, risks and how success will be judged.
- 02
Prototype
A working prototype on real examples, reviewed by the people who will use it.
- 03
Evaluate
Test sets for accuracy, safety and edge cases, with human review.
- 04
Harden
Security, permissions, guardrails, logging and cost controls.
- 05
Launch and improve
Phased rollout, feedback loops and regular evaluation.
Generative AI questions.
Will our data be used to train public models?
We use enterprise offerings and settings that do not use your data for training, and we confirm the data handling terms of every provider before use.
How do you stop the AI from making things up?
By grounding answers in your approved content, showing sources, limiting scope, testing on difficult cases and keeping people in the loop where accuracy matters.
Which model should we use?
The one that meets your quality, cost, latency and data requirements. We evaluate several on your tasks rather than picking by reputation.
Talk to us about generative ai.
Tell us which decision or process you want to improve and what data you have. We'll reply with whether it's a good fit and how we would test it.