Service 03 / 04
AI that does real work, measured like any other system.
Language models can read documents, answer questions, and carry out multi-step tasks. Making that dependable takes engineering: grounding in your data, evaluation against real cases, guardrails, and a clear place for human judgment. We build AI agents, assistants, and automations that hold up in daily use.
§ 01Business challenges
Where AI can help, and where it has disappointed.
Hours of reading and re-typing
Invoices, emails, forms, and reports are processed by hand, and skilled people spend their day moving information between systems.
Knowledge that is hard to find
Answers are buried in documents, wikis, and old tickets, so the same experts are interrupted with the same questions.
A pilot that never reached production
The demo was impressive. On real data it was inconsistent, and there was no way to measure whether a change made it better or worse.
Uncertainty about risk
Concerns about data exposure, wrong answers, and unpredictable costs keep useful ideas from moving forward.
§ 02System anatomy
What sits around the model.
The model is one step in the workflow. Reliability comes from the stages around it, and from a guardrail designed into each one.
01
Input
A document, message, or request arrives from a user or another system.
GuardrailInputs are validated, and sensitive data is detected and handled by policy.
02
Context
Relevant records and documents are retrieved from your own systems.
GuardrailRetrieval respects the permissions of the person making the request.
03
Model
A language model interprets the task and drafts a result.
GuardrailOutputs are structured and checked against a schema before use.
04
Tools
The system calls approved tools: lookups, searches, calculations.
GuardrailOnly allow-listed tools, each with narrowly scoped credentials.
05
Review
Uncertain or high-impact results are routed to a person.
GuardrailThresholds are agreed with the team that owns the outcome.
06
Action
The approved result is written back to the systems where work happens.
GuardrailEvery action is logged, attributable, and reversible where possible.
Measured in production
- Accuracy on an evaluation set
- Cost per task
- Latency
- Human review rate
§ 03Capabilities
What we build.
- AI agents & assistants
- Assistants that answer from your knowledge with sources, and agents that complete multi-step tasks within clearly defined permissions.
- LLM integration
- Language model features inside your existing applications, with structured outputs, fallbacks, and the model choice kept replaceable.
- Workflow automation
- Repetitive processes automated end to end, using plain rules where rules are enough and models only where judgment on text is needed.
- Document & data processing
- Extraction, classification, and summarization of documents at volume, with confidence scores and review queues for the uncertain cases.
- Evaluation & guardrails
- Test sets built from real examples, automated scoring, monitoring in production, and the controls that keep behavior within agreed limits.
§ 04Use cases
What this looks like in practice.
Typical shapes this work takes. These are illustrations of scope, not client case studies.
Example A
Document intake
Fields extracted from invoices, orders, or contracts and entered into your systems, with low-confidence results sent to a reviewer.
Usually involves
- Extraction
- Validation rules
- Review queue
- System integration
Example B
Internal knowledge assistant
Answers drawn from your documentation and records, with links to sources and access limited to what each person is allowed to see.
Usually involves
- Retrieval
- Permissions
- Source citations
- Evaluation
Example C
Request triage
Incoming emails or tickets classified, routed, and given a drafted reply that a team member approves or edits.
Usually involves
- Classification
- Drafting
- Human approval
- Analytics
Example D
Operational agent
Multi-step tasks such as reconciling records or preparing reports, performed across tools with approval before anything is changed.
Usually involves
- Tool use
- Scoped access
- Approval steps
- Audit log
§ 05Example solution
Docflow
AI document intake that reads freight paperwork and posts it to the ERP.
An AI-assisted pipeline that extracts data from invoices, bills of lading, and customs documents, validates it against the ERP, and sends only uncertain cases to a person for review.
Technologies typically involved
- Python
- LLM APIs
- PostgreSQL
- AWS
- Docker
- React
- REST APIs
§ 06Delivery
Measured before it is trusted.
Step 01
Choose one measurable task
We start with a task that has clear inputs, a clear definition of a good result, and enough volume to matter.
Step 02
Build an evaluation set
Real examples, with the correct outcomes agreed by the people who do the work, become the benchmark every version is tested against.
Step 03
Prototype and measure
We try approaches against the evaluation set and report accuracy, cost, and latency, including when the honest answer is that AI is not the right tool.
Step 04
Pilot with human review
The system runs on live work with a person approving results, so quality is confirmed before autonomy is extended.
Step 05
Run in production
Monitoring, alerts, and regular re-evaluation keep quality visible as data, models, and requirements change.
§ 07Questions
Common questions
Which AI models do you use?
It depends on the task. We compare models on quality, cost, latency, and data requirements using your evaluation set, and design the system so the model can be replaced as better options appear.
What happens to our data?
Data handling is decided before anything is built: which data the system may access, where it is processed, and the provider terms that apply, including options that exclude your data from model training or keep processing in your own cloud.
How do you know it works?
Every version is scored against an evaluation set built from real examples, and production performance is monitored. Quality is a number you can see, not an impression from a demo.
When is AI the wrong choice?
When the task follows fixed rules, needs exact calculations, or cannot tolerate occasional errors and has no review step. In those cases, conventional automation is usually cheaper and more reliable, and we will recommend it.
§ 08Start a project
Have a task that AI should be handling?
Describe the work, the volume, and what a good result looks like. We will reply with questions and an honest view of whether AI is the right tool.
- 01
Share the context
A short description of the project, your goals, and any constraints you already know about.
- 02
We review it
We read it carefully and reply with questions or an initial perspective on the problem.
- 03
A first conversation
A call to explore scope, approach, and whether we are the right fit for the work.