AI & Automation service
Generative AI
Build governed language-model workflows that help people find, interpret, and create information within defined knowledge and access boundaries.
Service context
Generative AI should be designed as part of a workflow, not added as an open-ended chat layer. We identify the task, the source material, the user decision, and the point at which human review remains necessary.
The solution can combine retrieval, model routing, prompt and tool controls, evaluation datasets, and usage telemetry while respecting the permissions already attached to enterprise information.
Problems addressed
- 01
Teams experiment with public tools while sensitive information, access boundaries, and retention expectations remain unclear.
- 02
Answers sound plausible but lack citations, reliable retrieval, or an evaluation set tied to the intended task.
- 03
A prototype cannot be operated because cost, latency, permissions, failure handling, and content change have not been designed together.
Engagement approach
Define a bounded workflow and evaluation set before selecting models, retrieval patterns, or interface behavior.
Preserve source permissions through ingestion and retrieval, then expose citations and uncertainty where users need to verify output.
Instrument quality, latency, cost, user feedback, and failure modes so the service can be changed through evidence rather than intuition.
Delivery stages
- 01
Bound
Select the workflow, users, knowledge sources, access rules, and review responsibilities.
- 02
Evaluate
Create representative questions, expected evidence, failure cases, and acceptance measures.
- 03
Integrate
Build retrieval, orchestration, interface, and tool connections around existing permissions.
- 04
Govern
Monitor quality, cost, latency, source change, user feedback, and approved model updates.
Technology context
OpenAI API
Azure AI
LangChain
PostgreSQL
Value direction
These are intended operating improvements, not guaranteed results.
- Faster access to relevant internal knowledge with a visible path back to the underlying source material.
- A reusable evaluation harness for comparing prompts, retrieval changes, and model options over time.
- Clear access, retention, and review controls suited to the information and workflow in scope.
- An operating view of adoption, quality, latency, and cost instead of an unmeasured conversational prototype.
Decision questions
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