Knowledge and RAG systems
Ingestion, retrieval, permissions, citations, and freshness designed around the knowledge users can actually trust.
Intelligence / Generative AI & LLMs
Generative AI & LLM Solutions
LLM applications grounded in the right context, connected to useful tools, and evaluated against the work they must perform.
Discuss your project01
The premise
The durable advantage is rarely the prompt. It is the system around the model: context, tools, evaluation, permissions, and feedback.
What the work includes
A complete system,
not an isolated feature.
Copilots and assistants
Conversational or embedded interfaces that help users reason, draft, search, and act inside existing product workflows.
Document intelligence
Structured extraction, comparison, classification, and review pipelines for high-volume unstructured information.
Model evaluation and routing
Task-specific test sets and controlled model selection balance quality, speed, privacy, and cost.
When this fits
Signals the system needs to change.
- 01Teams need useful access to complex internal knowledge
- 02A product needs a natural-language interface
- 03Document-heavy work is slow or inconsistent
- 04An LLM prototype lacks evaluation and production controls
Delivery model
Decisions stay connected
through production.
- 01
Discover
Align the business decision, user reality, technical constraints, and evidence needed to move.
- 02
Architect
Shape the product, experience, data, system boundaries, and delivery plan before complexity compounds.
- 03
Build
Work in reviewable increments with engineering, design, quality, and operations in the same loop.
- 04
Prove
Test behavior, usability, resilience, security, and performance against the conditions that matter.
- 05
Operate
Release with observability, ownership, and a clear path for the system to improve.
Tangible outputs
What moves from idea to operating reality.
01Use-case and information architecture
02Retrieval and model pipeline
03Integrated product experience
04Evaluation suite and operating controls
Useful answers
Generative AI & LLMs questions
01Is RAG always necessary?+
No. Retrieval is useful when answers depend on changing or private knowledge. Some tasks are better served by structured data, tools, fine-tuning, deterministic rules, or a combination.
02Can an LLM solution cite its sources?+
Yes, when the retrieval and interface are designed to preserve source identity and the product clearly distinguishes retrieved evidence from generated interpretation.
Have a system to build?
Make generative ai & llms concrete.
Share the decision, workflow, or product that needs to move. We will help make the next step concrete.
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