Define the workflow
Clarify the users, desired outcome, data, constraints and risk.
How we work
Build the system. Transfer the knowledge.
Start with a focused proof, then add the engineering needed for production. Production timelines reflect integrations, data, security, risk and scale.
Build a narrow working proof
For clearly scoped problems, often in 1–3 days. Agree the scope and commercial terms first.
Test with real users
Proceed, change direction or stop based on what the proof shows.
Engineer for real operation
Integrations, security, permissions, evaluation, failure handling and monitoring.
Train and document
Teach the team, hand over operating guidance and agree maintenance responsibilities.
Use operating evidence
Refine quality, cost and response times using real outcomes.
Training & enablement
Your team should understand what it operates.
Role-specific training
Practice the workflows people actually use, including how to review AI output.
Operating playbooks
Document authority, exceptions, provider accounts, troubleshooting and escalation.
Cost and maintenance
Explain usage, budgets, model choices, configuration and ongoing responsibilities.
Trust principles
AI you can control.
Your business comes first
We use conventional software when it solves the problem more reliably or economically.
Human control where it matters
Define approval boundaries for high-impact or irreversible actions.
Transparent operating costs
Monitor usage, route work to suitable models, cache results and set budgets.
Ownership without unnecessary lock-in
Your data stays yours. Use client-owned provider accounts where practical. Code and IP terms are agreed in the contract.
Authoritative knowledge
Generated output does not silently replace your source-of-truth business data.
Reliability and security by design
Validation, bounded retries, monitoring and access controls match the needs of the project.
Are we locked into a provider?
Provider-independent interfaces can make migration easier when portability matters. Model behavior and feature differences still need evaluation.
Can you train our team?
Yes. Role-specific training, documentation, operating playbooks and cost-management guidance can be part of the engagement.
Can a whole workflow run automatically?
Sometimes. Autonomy should match the risk, with people reviewing high-impact decisions and exceptions.
Do you work globally?
Yes. Mikisi is Chicago-based and works remotely with organizations worldwide.
What if the problem does not need AI?
We will say so. The objective is to solve the problem, not force AI into it.
A useful next step
Bring us the problem.
We build the system.
You do not need a technical specification. Tell us what happens today and what you want to change.