Intake and qualification
Read inbound information, apply approved criteria, capture missing details, and route the work to the right queue.
AI automation
AI automation that moves work through the systems you already use
We build guarded AI workflows for intake, qualification, routing, drafting, data extraction, and customer communication. Each workflow has a defined job, inputs, limits, review path, and fallback.
Build map02
AI should move a real job forward.
- Intake and qualification
- Structured data extraction
- Assisted drafting and review
- CRM and operations workflows
The decision
Start with the repeated decision, not the model
The useful opportunity usually appears where someone repeatedly reads, decides, writes, routes, or updates a system. We find that work before choosing the AI layer.
Inputs need triage
Leads, requests, documents, or conversations arrive in different forms and a person repeatedly decides what should happen next.
Knowledge is scattered
The answer lives across policies, product notes, prior conversations, and a few people who carry the process in their heads.
The answer must trigger work
A useful result needs to create a task, update a CRM, prepare a record, notify a reviewer, or continue a workflow in another system.
Customer-facing AI needs boundaries
Sensitive topics, uncertain answers, brand voice, tool permissions, and escalation rules need explicit treatment before a bot meets a customer.
Capabilities
AI automation built as an operating system
A prompt is one component. Production work also needs source context, permissions, tool calls, review states, logs, cost controls, and a path when the model cannot safely continue.
Extraction and classification
Turn documents, messages, and free-form submissions into structured fields that another system can validate and use.
Assisted drafting
Prepare replies, summaries, proposals, or content from approved facts, with review before consequential work leaves the system.
Knowledge interfaces
Answer questions from controlled source material and show the operator where the answer came from when the use case requires it.
Tool-using workflows
Connect the AI decision to tasks, records, notifications, CRM updates, and other actions under limited permissions.
Review, logs, and fallback
Keep uncertain work visible, preserve the input and result, cap retries, and hand the task to a person when the system reaches its limit.
How we work
From the real workflow to a controlled release
We prove one narrow workflow against real examples before asking the organization to trust a broader system.
- 01
Trace the current work
We follow the task from arrival to completion and record inputs, decisions, exceptions, systems, owners, and the current cost of delay or rework.
- 02
Write the risk contract
We separate suggestions from actions, define required review, restrict tools and data, and name the cases that must stop or escalate.
- 03
Build one production loop
The first workflow uses representative examples, observable outputs, explicit failure handling, and the same systems the team will use after launch.
- 04
Measure and expand
We review acceptance rate, exceptions, latency, model cost, operator time, and the quality of the final business outcome before adding another job.
Proof and resources
See the work behind the service
Our public work spans voice interfaces, AI-assisted customer communication, and the developer tools used to move AI Studio projects into owned repositories.
Questions before scope
The choices that change the build
These answers shape architecture, effort, and the safest first release.
Does the AI need to act on its own?+
No. Many useful systems prepare a decision, draft, or structured record for a person to approve. Autonomy is a product choice tied to risk, reversibility, and evidence, not a requirement.
Can it work inside our current tools?+
Usually, if those tools expose suitable APIs, webhooks, exports, or other supported connection points. We map the systems and permissions before promising a specific action.
How do you handle sensitive or consequential work?+
The design uses limited access, explicit review states, logs, escalation, and deterministic checks around the model. High-impact actions stay with a person unless the use case and controls support something narrower.
How do we avoid a demo that falls apart in production?+
We test representative inputs, missing data, uncertain outputs, tool failures, latency, and cost. The first release includes the failure path because the team will eventually meet it.
Related services
Start with the real constraint
Choose one repeated job and make it observable
Bring the examples, exceptions, and systems involved. We will map where AI can help, where rules are stronger, and where a person should keep the decision.