Service
Make the AI feature work beyond the demo.
Retrieval, agent workflows, and evaluations built around the data and failure cases your product actually encounters.
The problem
What this actually looks like
A convincing demo does not show how a system behaves with unfamiliar documents, incomplete inputs, or a model change.
Start by identifying the outputs that matter and the failures you need to detect. That gives the engineering work a measurable target.
Deliverables
What you get
Concrete artifacts, not activities. Everything below is something you can point at and say you received it.
- Retrieval and data-flow design
- Evaluation cases drawn from product behavior
- Cost and latency instrumentation
- Documented fallbacks and operational guidance
Timeline
How it works
Ranges, not best cases. The dates below are what it usually takes, including the week something goes wrong.
- 01Inspect
Review the current pipeline
Understand the inputs, retrieval steps, model calls, and known failure cases.
- 02Measure
Establish an evaluation baseline
Define representative queries and the criteria used to judge their outputs.
- 03Improve
Implement and compare
Evaluate changes against the baseline and inspect the failure cases that remain.
- 04Operate
Prepare for change
Document model dependencies, monitoring, and the process for evaluating future changes.
Approach
Our approach
A pipeline is easier to improve when its decisions and failures are visible. We favor clear retrieval boundaries and evaluations that reflect the product.
What we avoid
- Treating a successful demo as proof of reliability.
- Using a framework that hides the behavior the team needs to inspect.
Engagement
Engagement options
Shape
Review or scoped implementation
Pricing
Quoted for your scope
The data sources, evaluation needs, integrations, and operating constraints determine the work.
View engagement optionsFAQ
Frequently asked questions
Can you work with our current model provider?
Include your provider and infrastructure in the inquiry. We can then discuss the integration and any constraints before proposing changes.
How do we measure improvement?
Use representative inputs and explicit acceptance criteria. An evaluation set provides a repeatable comparison between versions.
How is private data handled?
Data access, hosting, retention, and provider settings need to be agreed for the engagement before private data is shared.