Generative AI consulting & development
Create enterprise assistants and document applications that use approved knowledge, expose evidence and keep important decisions under human review.
Build GenAI around evidence and evaluation.
We help enterprise teams prioritise GenAI use cases and develop retrieval-augmented assistants, document workflows and supervised agents. The architecture connects approved data sources with model evaluation, permission-aware retrieval, human review and observability. Deployment and LLMOps include quality tests, latency, cost monitoring and controlled releases.
Select a task with accessible reference material and reviewers who know what a good answer looks like. We build a test set, inspect access boundaries and compare candidate approaches before integrating the application into daily work.
Business applications.
Explore the related solutions this service supports.
Preparing grounded knowledge summaries
Retrieval-assisted drafting reduces preparation time while retaining human verification.
Assume 2,000 instances per month, with average time reduced from 10 to 6 minutes per instance.
Less time per instance
Monthly capacity released
Assumed time before and after
Measure: Human handling time, including validation and exceptions; validate quality and total operating costs.
Calculation: (10 − 6) ÷ 10 × 100 = 40%; 2,000 × (10 − 6) ÷ 60 = 133.3 hours. Figures rounded where needed.
Illustrative assumptions, not a client result, forecast or guarantee. Validate the baseline and outcome in your own environment. Capacity released does not equal cash savings; account for quality, utilisation, implementation, software and ongoing support costs.
A starting point for this work.
Explore focused workflows that connect with this service.
A clearer starting point.
When is retrieval-augmented generation a good fit?
It is useful when answers depend on changing or organisation-specific documents that users must be authorised to access. Retrieval still needs evaluation: finding a passage does not guarantee the generated answer is supported.
Questions worth exploring.
Practical reading to help you assess the approach and prepare the right brief.
Enterprise GenAI: choosing the right first use case
Compare candidate GenAI tasks by evidence, risk and ease of evaluation.
Read guide 3 minRAG vs fine-tuning: which approach fits your business?
Choose between retrieval and model adaptation based on the problem you need to solve.
Read explainer 3 minAI tender intelligence: from discovery to a bid/no-bid decision
Connect tender discovery, qualification and bid preparation with human review.
Read guide 3 minWhat does your next move need?
Bring us a business priority. We’ll work with you to define the opportunity, the scope and the next step.