Productizing an AI capability
The model can do something impressive, but the product form, interaction, architecture and launch path are unclear.
06 AI Applications & Agents
AI Applications & Agents
Turn model capability into product: agents, workflows, skills and knowledge systems.
From use-case definition to production, we build AI applications that can be evaluated, observed and maintained. Agents, tool calling, RAG, workflows, MCP / skill integrations, human review and continuous improvement.
FIT
Who this is for
A demo only needs to work once. A product needs to work every day. Evaluation, exception handling, permissions and observability matter as much as model choice.
An internal agent that must act
You need an agent that queries data, calls systems and completes process steps — not just answers in a chat box.
A prototype that is not reliable
Quality swings, there is no evaluation or logging, and releasing it to real users feels risky.
OUTCOMES
What you walk away with
Not a feature list — outcomes that enter the business, get measured and can be owned.
01
An explicit agent boundary
Define what it can and cannot do, when it calls a tool, and when it hands control to a human.
02
An evaluable quality system
Task sets, scoring criteria and regression tests so every model, prompt or knowledge change can be compared.
03
Useful, safe tools and skills
Business systems wrapped as reusable tools with authorization, timeouts, retries and idempotency.
04
A production system you can operate
Logging, tracing, cost monitoring, human review and staged rollout — built for iteration, not a one-off launch.
DELIVERABLES
What is delivered
Exact scope is tailored per project. Every deliverable has a clear acceptance method.
Use-case spec and human–AI boundary
Agent / workflow architecture
RAG knowledge base and retrieval strategy
Tool, API and skill integrations
Evaluation set, scorers and regression report
Production deployment, monitoring and runbooks
PROCESS
How we deliver
Every stage has a reviewable output, so the project never becomes a long black box.
01
Frame
Define user, task, success criteria and cost of failure. Decide what AI does and what a human must confirm.
02
Architect
Choose models, retrieval, tool use and orchestration. Design state, permissions, exceptions and cost boundaries.
03
Build
Ship a thin end-to-end slice first, then add tools and knowledge. Every capability gets evaluation and logging.
04
Evaluate & ship
Run regressions on real tasks, use staged rollout and human review, then monitor quality, cost and latency in production.
USE CASES
Where it shows up
These are typical entry points, not fixed templates. The real scope starts with your workflow.
Research and analysis agent
Collect sources, extract evidence and produce a cited brief with human review and follow-up questions.
Sales operations assistant
Query the CRM, prepare account briefs, update opportunities and create follow-up tasks.
Document processing pipeline
Parse contracts, invoices and reports; extract fields; validate rules; route into approval.
Enterprise knowledge assistant
Retrieve permission-aware internal knowledge, cite sources and escalate explicitly when uncertain.
STACK
Tools and technology
Tools serve the use case. We stay technology-neutral and reuse your existing systems first.
OpenAIAnthropicGeminiDeepSeekQwenLangGraphDifyn8nMCPpgvectorCloudflare WorkersPython
FAQ
Common questions
Something else? Email us. We usually reply within one business day.
How is an agent different from a chatbot?
A chatbot mainly generates text. An agent can plan steps, call tools, read and write business systems, and adjust based on results. That extra capability demands stricter permissions, evaluation and human fallback.
How do you ensure accurate answers?
No method guarantees 100% accuracy. We constrain sources, require citations, define refusal conditions, validate with tools, add human review and run regression tests — so errors are bounded, detectable and traceable.
Will our data be used for training?
By default, we do not use client data for training. The exact answer depends on model-provider terms and deployment. We document data flows, retention and de-identification during architecture.
Should we buy a platform or build custom?
If a general platform covers 80% of the need, configure and adopt it first. Build custom only where workflow, permissions, integrations or evaluation require it. We do not write code for the sake of writing code.
Who maintains it after launch?
We hand over monitoring, evaluation and runbooks, and can provide ongoing optimization. The ideal state is a client team running daily operations while we handle complex changes and quarterly reviews.
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Start a concrete conversation
Is this use case worth building?
Thirty minutes to lay out the goal, workflow and constraints. We decide the next step together.