03 AI Systems
AI that does real work.
Practical AI that does real work inside your business, built as dependable software: measured, monitored and designed with people in the loop.
- Agents
- RAG
- Automation
- LLM integration
- Evaluation
Interactive demonstration
No AI model is called — every output is pre-written to show how a production pipeline behaves.
Input · Customer email
Received via help desk · plan: Growth
“Hi — we were charged twice for our March invoice (INV-20931). Can you refund the duplicate? Also, how do I add a second admin to our workspace?”
Run the workflow, or step through it one stage at a time. Select any completed stage to inspect what happened there.
Outcomes
Practical AI, measured like any other system.
- 01
Work that gets done
AI aimed at specific, valuable tasks — triage, extraction, drafting, research — rather than open-ended demos.
- 02
Answers you can trust
Retrieval grounded in your own sources, with citations, confidence handling and clear fallbacks.
- 03
Reliability you can see
Evaluation suites, tracing and cost monitoring so you know how the system behaves before and after launch.
Capabilities
What we build
We focus on specific, valuable tasks inside your business and build the surrounding software that makes AI dependable.
- AI agents
- Task-focused agents that use tools, follow policies and escalate to people when confidence or permissions require it.
- AI-powered applications
- Products where language models are a core feature: assistants, copilots, search and content workflows.
- Workflow automation
- Multi-step processes that combine models, business rules and your existing systems, with human approval where it matters.
- LLM integrations
- Model selection, prompt design, structured outputs and provider abstraction so you can change models as they improve.
- Retrieval-augmented generation
- Ingestion pipelines, chunking, hybrid search and re-ranking that ground responses in your documents and data.
- Knowledge assistants
- Internal assistants that answer questions from policies, wikis and tickets, with permissions respected and sources cited.
- Document processing
- Classification and extraction from contracts, invoices, forms and reports into structured, validated data.
- AI-enabled internal tools
- Back-office tools that draft, summarize and route work so teams can focus on decisions.
- System integrations
- Connecting AI capabilities to your CRM, help desk, data warehouse and line-of-business systems.
- Evaluation, reliability and monitoring
- Test sets, automated evaluations, guardrails, tracing, cost controls and regression checks for every change.
Reliability
Dependable AI is an engineering problem.
Models are only one component. The reliability of an AI system comes from the engineering around it — and that's where we spend most of our effort.
- 01
Evaluate
Success criteria and a test set of realistic examples are agreed before we build.
Automated scoring on every prompt, model or pipeline change — regressions block release.
- 02
Ground
Responses are based on your documents and data, retrieved with permissions applied.
Citations on every answer; the system declines when sources are insufficient.
- 03
Guard
Structured outputs are validated against schemas and business rules.
Input and output policies, PII handling, tool permissions and spend limits.
- 04
Observe
Every run is traced: inputs, retrieved context, tool calls, outputs, latency and cost.
Quality and cost dashboards, sampled reviews and alerts on drift.
- 05
Involve people
Consequential actions and low-confidence results go to a person.
Review queues with context, one-click corrections that feed the evaluation set.
Deliverables
What you receive
Scope is tailored to each project. A typical engagement includes:
- Use-case assessment and feasibility review
- Evaluation dataset and success criteria
- Working prototype tested on your data
- Production pipeline with guardrails
- Integration with your systems and identity
- Observability: tracing, quality and cost dashboards
- Human review and escalation workflows
- Model and prompt change process
Common starting points
Teams usually come to us when…
Support triage
Classifying and routing incoming requests, drafting replies for agents to approve.
Internal knowledge
Letting teams ask questions of policies, documentation and past work, with sources.
Document intake
Turning unstructured documents into validated records in your systems of record.
Research and drafting
Assembling briefs, summaries and first drafts from many sources for expert review.
Related concepts
AI Systems in practice
Concept projects that show our approach. They're labeled as concepts and present no real-world results.
AI Systems FAQ
Common questions
Which AI models do you work with?
We work with leading commercial models from providers such as Anthropic, OpenAI and Google, as well as open-weight models that can be self-hosted. We choose based on quality for your task, data requirements, latency and cost, and we design so models can be swapped later.
Is our data used to train models?
We configure provider accounts and agreements so your data isn't used for training where the provider offers that option, and we can keep sensitive workloads on infrastructure you control. We'll document exactly where data flows.
How do you know the AI is working well?
Before building, we agree success criteria and create an evaluation set from realistic examples. Every significant change is measured against it, and production traffic is traced and sampled for review.
What if the AI gets something wrong?
We design for it: confidence thresholds, validation of structured outputs, citations, human approval for consequential actions and clear escalation paths. The goal is a system whose failures are caught, not hidden.
Have a process AI could improve?
Tell us about the work involved. We'll give you an honest view of where AI can help, where it can't, and what it would take to build.