Service
From trying AI out to running it as part of the business.
We embed generative AI, RAG, and agents into real business workflows — from selecting where AI applies, through quality and safety design, to the move into production.
Problems this addresses
- You want to adopt AI but cannot decide which processes it applies to.
- A PoC works, but there is no path into production.
- Output quality and safety have not been designed for.
- There is no one in-house who can own the implementation.
The state we aim for
The target workflow is defined, quality and safety criteria are written down, and the people doing the work use the system every day — with a loop in place to keep improving it.
Scope
What the work involves
- 01
Identify where AI applies
We decompose the workflow and separate the steps where AI helps from the steps that require human judgement, so investment does not go where it will not pay off.
- 02
Design data, RAG, and agents
We decide where reference data lives and how it is updated, the granularity of retrieval, and the scope of authority handed to agents — the parts that force a rewrite if left vague.
- 03
Design quality, safety, and human approval
We turn “correct output” into an evaluation procedure and make explicit where human approval is required. Without evaluation, no one can say whether the system improved.
- 04
Move from PoC to production
We put monitoring, cost controls, and fallback paths in place before going live. What we hand over is not a system that works, but one that keeps working.
Key deliverables
- •Selected target workflows, and the reasons for what was excluded
- •Design documentation for data, retrieval, and agents
- •An evaluation procedure and the record of results over time
- •The production application and its operating procedures
How responsibilities divide
Rainpulse handles
- Decomposing the workflow and selecting where AI applies
- Design, implementation, and the evaluation harness
- Production cutover and the improvement loop
You provide
- Access to the people who own the target workflow
- Access to reference data and existing systems
- Final say on quality criteria
Engagements
Related engagements
AI / LLM
LLM features for an education product
The LLM features went live as a supported part of the product, with an evaluation procedure in place to keep improving them.
AI / Web
AI knowledge search and a healthcare AI platform
RAG-based internal knowledge search entered daily use, and optimisation of both frontend and backend of the platform was completed.