The same manual work comes back every week
People gather the same inputs, apply the same rules, prepare the same output, and chase the same approvals each week.
Turn one manual workflow into a production AI system.
We choose a narrow use case, connect it to the tools and data it needs, test the result, and launch it with the people who do the work. Expand only after the first version proves useful.
One workflow
One measurable result
Start where the current cost, delay, error rate, or missed opportunity is visible.
4–8wk
Typical first release
A focused build can usually reach users within two months.
Your repo
Code your team can operate
Keep the system and runbook, or keep us involved after launch.
The model is often the smallest part of the implementation.
A useful implementation needs approved data, permissions, integrations, business rules, evaluation, failure handling, and a place in the existing job. A model call alone does not provide any of them.
We start where the cost or delay is visible. Sound systems stay. Weak data or handoffs get only the repair needed to make this workflow reliable.
Choose a workflow that can prove its value
A strong first use case repeats, costs enough to notice, and has an owner who can tell a good result from a bad one.
People copy data between systems
Important context moves through copy and paste, spreadsheets, email, or one person’s memory.
The demo never became part of the job
A model or chatbot exists, but it never got the permissions, data, evaluation, review steps, or integrations required for daily use.
Off-the-shelf software stops too early
The common path works, but your business rules, integrations, and awkward cases still fall back to the team.
Move from a promising use case to a controlled release
Each stage reduces uncertainty before the system receives more responsibility.
- / 01
Measure the current work
Count the time, errors, delays, missed revenue, or other cost. Name the person who owns the result.
- / 02
Choose the boundary
Decide what normal software can handle, where a model adds value, and which decisions still need a person.
- / 03
Build the whole path
Connect the data and tools, build the user path, test representative cases, and make failures visible and recoverable.
- / 04
Put it into the job
Release with a small user group, fix the missing cases, and document support, cost, and ownership.
The work around the model is part of the product
We own the product and engineering work required to turn one use case into a system people can use.
- Workflow mapping, a baseline, and a clear production scope
- AI agents with tools, permissions, review paths, and versioned evaluations
- Automation for intake, routing, reporting, approvals, and handoffs
- APIs and integrations across company systems and model providers
- Internal applications, dashboards, and customer-facing product work
- Deployment, monitoring, audit trails, a runbook, and launch support
Use the simplest reliable design
The answer can be one model step, an agent, an integration, a small internal tool, ordinary automation, or a combination.
Keep the sound parts of your stack
Keep the ERP, CRM, warehouse, support platform, or internal database. Add the workflow through the interfaces already there.
Fix only what is blocking the work
Create the smallest data or workflow layer needed for reliable execution. One implementation should not become a company-wide replatform.
Four first projects with clear operating boundaries
A narrow result is easier to test with real cases, easier to adopt, and cheaper to change.
Recurring reporting
Collect the source data, apply company rules, prepare the output, and flag anything unusual.
Client or vendor onboarding
Keep documents, tasks, approvals, messages, and exceptions in one visible flow.
Operational document processing
Read incoming material, check required fields, update the system of record, and route uncertain cases to a person.
Focused internal tools
Give the team one place for the information, actions, approvals, and history needed to finish the job.
Stage the budget around specific decisions
Start with a scope that can reach users and show whether a wider investment is justified.
sprint1 week
$5,000
Map the use case and ship a narrow working slice or technical proof in your repository.
mvp~1 month
From $20K
Build one end-to-end workflow, with product design, integration, deployment, and handover.
scale-up2+ months
$75K–$250K
A dedicated team for implementation across several systems, teams, or higher-risk operations.
Questions to settle before the work starts
How do you choose the first AI use case?
We look for repeated work with a visible cost, usable inputs, a clear owner, and someone close to the job who can judge the output. We also check whether a simpler automation can solve it without AI.
Will you replace our existing systems?
Only when a missing or weak system blocks the result. If the current systems are sound, we integrate with them and leave them alone.
How much does AI implementation cost?
A one-week sprint is $5,000, an MVP starts at $20,000, and larger multi-system implementations typically range from $75,000 to $250,000.
Which AI providers do you implement?
We work with Claude, OpenAI, Gemini, smaller or self-hosted models, and ordinary automation. We choose based on quality, privacy, speed, and cost.
What happens after launch?
Your team gets the code and documentation. We can hand over completely or stay for monitoring, maintenance, and improvements.
How do you handle security and human review?
We define data boundaries, permissions, review steps, escalation paths, logs, and stop conditions before the system handles live work.
Show us the workflow that should be easier by now.
Tell us what repeats, which systems are involved, where the work stalls, and who owns the result. We will suggest the smallest first scope worth testing.