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Data, knowledge & model optimization

Better answers from the information you already have, at a cost you can predict.

When an AI assistant gives weak answers, the cause is often the information behind it: outdated documents, duplicates, missing context, or poor retrieval — the step where the system finds the right passages to answer from. We clean up and structure the inputs, tune how information is retrieved, and compare models so you aren't paying for more than you need.

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When this helps

Your AI assistant gives vague, outdated, or inconsistent answers.
Information lives across drives, wikis, and systems with no clear source of truth.
Model costs are growing faster than usage.
You're not sure whether a cheaper model would do the job.

What AgentSpring does

1
Audit the sources
We review what documents and data feed the AI, and flag duplicates, gaps, and outdated material.
2
Structure the knowledge
We organize content, add useful metadata, and set ownership so it stays current.
3
Improve retrieval
We tune how content is split, indexed, and searched so the right information reaches the model.
4
Compare models and costs
We test candidate models against your evaluation set and estimate cost at your expected usage.

What you receive

These are potential deliverables. Your proposal defines the exact deliverables and responsibilities; not every item applies to every engagement.

Source audit with recommended clean-up
Restructured knowledge base or indexing setup
Before-and-after evaluation results
Model comparison with cost estimates
Content ownership and update guidelines

What we need from your team

Good results depend on your team's involvement.

Subject-matter experts to confirm what's accurate
Access to source content and current AI setup
Example questions real users ask

How success is assessed

Measurable improvement on an agreed evaluation set
Clear owners for keeping content current
A cost estimate you can budget against

What affects scope, timing, and cost

Volume and variety of content
Number of data sources
Whether an evaluation set already exists
Platform and model options under consideration

Projects are scoped around your goals, existing systems, and the work required. Before work begins, we agree on deliverables, responsibilities, fees, and any ongoing software or support costs.

Where relevant, third-party licenses, hosting, model usage, and ongoing support are separate costs identified before work begins. Your proposal also defines integration access, ownership arrangements, handover, and support scope.

How pricing works

Illustrative example

Example: an HR policy assistant giving stale answers

An assistant keeps citing a superseded leave policy because old versions sit next to new ones. Removing duplicates, marking effective dates, and adjusting retrieval to prefer current versions would address the root cause instead of patching the prompt.

See work & examples

Questions about this service

Do we need a data warehouse first?

Usually not. We start with the sources the AI actually needs.

Can you reduce our AI bill?

Often there are savings in model choice, caching, or prompt design. We'll measure before promising anything.

What is an evaluation set?

A list of realistic questions or tasks with known good answers, used to test the system consistently.

Related services

Talk through data, knowledge for your business.

The free workflow review is your starting point. Bring a project, a process, or a question about where to start.

What happens on the first call?

We’ll discuss what you want to improve, the tools you use, and the constraints you’re working within. You’ll leave with 2 to 3 automation ideas, whether or not we work together.

Start with a defined project or an uncertain problem. You’ll leave with 2 to 3 automation ideas, whether or not we work together.