AI connected to real workflows, not a chat box with a logo
Language models are useful for reasoning, language, classification and generation. They are not a replacement for a database, a rule, or a person who is accountable. We connect AI to the systems you already run: retrieval, tool calling, assistants, agents, and human-in-the-loop steps, with evaluation, monitoring and cost in the design.
How we treat models
Not every problem needs AI. Sometimes a simple rule or a database query is the better solution. We use traditional software when traditional software is enough. We use AI when the input is messy language, a document, a classification, or a choice among a few approved tools.
Problems this work is for
- People are the integration layer
Staff copy between email, CRM and a spreadsheet. An assistant or agent can draft or execute the transfer, with a rule for when it must wait.
- Documents arrive faster than they can be read
Invoices, applications, tickets or contracts need extraction, classification and routing, not a summarizer toy.
- Search does not find the policy
Internal knowledge is scattered. Retrieval-augmented answers with citations beat an ungrounded chatbot.
- A vendor demo that cannot use your tools
The model talks well and cannot create a ticket, update a record or respect role limits. Tool calling is the work.
- Nobody can explain what the AI did last Tuesday
You need traces: input, retrieved chunks, tool calls, outputs and who approved.
- Fear of silent mistakes
High-impact actions stay behind human review. Low-risk actions can auto-complete once you have seen the work hold up.
What we typically build
- Workflow-shaped assistants
Guided jobs with forms and retrieved context, not an unbounded chat that invents policy.
- AI agents with tool access
Task-specific agents that choose among approved APIs: CRM, email, internal HTTP, queues.
- Document and ticket processing
Extract, classify, route, and write structured fields your software already understands.
- RAG and retrieval
Answers grounded in your documents, with access control that matches the application.
- Evaluation, monitoring and cost
Golden examples, regression checks when prompts or models change, traces, and spend you can see.
- Human review queues
Approve, edit or reject before the action lands in a customer-facing system.
Where this usually shows up
- Operations copilots
Draft a reply, propose a next status, or fill a form from an email, operator clicks send.
- Content pipelines with editorial control
Research and draft stages that never publish without a human.
- Support and knowledge
Answers grounded in your help center or runbooks, with “I don’t know” as a first-class output.
- Internal tools in technology companies
Agents on engineering or ops workflows where APIs already exist and permissions are strict.
How an AI engagement runs
- Discovery
Name the trigger, the system of record, what the model must never do, and whether the data can legally leave your environment.
- Scope
One workflow. Success examples. Failure examples. Review policy. Vendor constraints.
- Architecture
Retrieval, tools, identity, logging, secrets, fallbacks when the vendor is down, and cost limits.
- Build and evaluate
Ship behind a flag. Measure on your set. Tighten tools before you “make the prompt nicer.”
- Launch with monitoring
Traces, cost alerts, and a path to turn the system off. Then iterate on real misses, not demo scripts.
Name the action the model would take
Send the trigger, the tools it would need, and what must wait for a human. We will tell you if this is an agent, a classifier, a retrieval assistant, or a deterministic workflow that does not need a model.