# AI solutions

> Practical AI applications that help your team find knowledge, understand documents, and automate work with evaluation and human oversight.

AI with a job to do.

Start with a useful problem, not a model. We build AI into the workflows where it can help, then test the output against the standards your business actually needs.

- URL: https://weaveward.com/services/ai-solutions
- Industries: Retail, Logistics, Insurance, Healthcare, Events

## Common problems

### Useful knowledge is hard to find

Answers live across documents and tools, so people repeat research or ask the same questions again.

### Manual document work slows the team down

Reading, classifying, and extracting information consumes time that could go to higher-value decisions.

### A promising AI demo is not a dependable tool

Business use needs evaluation, access controls, understandable limitations, and a path for human review.

## What we build

### Knowledge assistants

Search and ask questions across approved company documents, with source references and access boundaries.

### Document processing

Extract and structure information from forms, receipts, and business documents, with review for uncertain results.

### Workflow copilots

Help teams draft, classify, summarize, or prepare actions inside an existing process.

### AI pilots and evaluation

A limited, measurable pilot with a relevant test set, quality criteria, and a decision about the next step.

## Technology

- **Models**: LLM APIs, Open-source models, Python
- **Knowledge**: RAG, Vector search, Document pipelines
- **Reliability**: Evaluation datasets, Human review, Access controls

## Related project studies

- [Claim Triage](https://weaveward.com/work/motor-insurance-claim-triage): Prototype, 2026. An insurance review workspace linking claim documents, policy and repair checks, and traceable recommendations for a human officer
- [CVAT Ball Auto-Labelling](https://weaveward.com/work/ball-auto-labelling-cvat): Prototype, 2026. A custom CVAT adapter combining full-frame and tiled inference for small footballs, producing editable ball and person annotations
- [Finly](https://weaveward.com/work/finly-personal-finance-split-bill-app): Prototype, 2026. A personal-finance MVP connecting receipt review, shared bills, realtime item claims, and traceable settlement on a balanced ledger

## Frequently asked questions

### How do we know whether AI is right for our use case?

We look at the task, the available data, the consequences of mistakes, and the current cost of doing the work. A small pilot with agreed evaluation criteria helps determine whether AI offers a practical improvement.

### Can an assistant answer from our own documents?

Yes. A retrieval-based approach can use approved source material to inform answers and provide references. We still test answer quality and define what the assistant should do when the source material is insufficient.

### Will the AI always give the correct answer?

No. AI output can be incomplete or incorrect. We design evaluation, source grounding, validation, and human review around the risk of the task instead of treating generated output as automatically authoritative.

### Can we control where our data goes?

We review data sensitivity, provider terms, deployment options, and access requirements during discovery. The architecture and provider choice are agreed around those requirements before connecting business data.