Applied AI engineering

AI, RAG and agent development

We turn a useful AI idea into a product workflow people can trust. That means choosing the smallest sufficient system, grounding outputs in evidence where needed, and designing evaluation and human review before automation reaches a consequential decision.

[ 01 ]

What we build

RAG and semantic search

Retrieval pipelines that combine prepared source data, vector search, filters, citations, and clear fallbacks when evidence is incomplete.

Agent workflows

Explicit, observable workflows for research, generation, review, scheduling, and other tasks that genuinely benefit from specialized stages.

AI product integration

Recommendations, extraction, classification, search, and assisted decisions embedded into the product rather than isolated in a demo chatbot.

Evaluation and safeguards

Representative test sets, structured outputs, approval gates, recovery paths, and measurements across quality, latency, and cost.

[ 02 ]

How the work moves

STEP 01

Define the decision or job

We identify the user outcome, available evidence, acceptable failure modes, and the point where human judgment must remain in control.

STEP 02

Prototype against real examples

A narrow working slice tests retrieval quality, model behavior, interface choices, latency, and cost before the architecture becomes expensive to change.

STEP 03

Engineer the operating workflow

We add data preparation, permissions, queues, observability, evaluations, approval states, and failure recovery around the model call.

STEP 04

Improve with evidence

Production feedback is converted into test cases so prompt, retrieval, workflow, and model changes can be compared rather than guessed.

[ 03 ]

The outcome we work toward

A focused AI feature tied to a measurable user job
Source-aware answers and traceable workflow states
Human approval at the appropriate risk boundary
An architecture that can change models without rebuilding the product

[ 04 ]

Related practical guides

What a GenAI Project Needs After the Demo Works Six GenAI Decisions That Prevent Expensive Rebuilds When Multi-Agent AI Is Worth the Complexity

Common questions

Do we need a multi-agent system?

Usually not at the start. We begin with one model and an explicit workflow, then add specialized agents only when the work has distinct responsibilities, tools, or review boundaries.

When is RAG appropriate?

RAG is useful when answers depend on current or private source material that must be retrieved at request time. It is not a substitute for clear source preparation, permissions, and evaluation.

Can you take an AI prototype into production?

Yes. We assess the prototype, preserve validated product knowledge, and add the data, evaluation, security, recovery, and operational boundaries required for dependable use.

Bring us the problem, not a prescribed stack.

We will help define a sensible first step and make the technical tradeoffs visible.

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