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Implementation

The situation we're built for

You have a specific problem. You know what AI should solve.

What you don't have is the right team to deliver it, and the prospect of recruiting, onboarding, and managing an internal AI function feels disproportionate for one use case. Hiring a consultancy means a six-figure quote, a six-month timeline, and a system you'll inherit at the end with no one to maintain it. There has to be something in between.

What we deliver, three commitments

A dedicated team

A small, focused team owns your project from scoping through production. The same people who scope the problem are the people who build the solution. No handoffs between sales, scoping, and delivery.

A system that runs inside your environment

Everything we build is deployed on your infrastructure. Your data never leaves. Your IT and security teams stay in control. Compliance built in from the start, not retrofitted.

Ongoing maintenance

We don't hand the system over and disappear. The monthly subscription covers ongoing maintenance, support, and platform updates as they become available. If issues arise, we address them, and if your operational requirements evolve, we work with you to assess and implement the appropriate changes.

Step 01

Scoping conversation

We understand the problem, the constraints, and what success looks like.

Step 02

Proposal and pilot

A clear scope, a fixed monthly fee, and a target production date.

Step 03

Build and deploy

Typically eight to twelve weeks from kickoff to production.

Step 04

Ongoing operation

Monthly subscription covers everything from monitoring to evolution.

Reason 01

Subscription, not invoice

Monthly subscription means no six-figure upfront risk.

Reason 02

Weeks, not quarters

Production reached in weeks, not quarters.

Reason 03

Continuous ownership

The team that built it is the team that maintains it.

Reason 04

Incentive-aligned

Cancel anytime if it's not delivering, our incentive depends on it working.

Case Study Research

Delivering a grant matching engine
for a research organization without an AI team.

A Boston research organization had been hearing the same request from their customers for years. They had the demand and the domain expertise, they didn't have the engineering function to build the AI system or operate it.

The situation

  • The organization's customers had been requesting automated grant matching for years, manually cross-referencing researcher profiles against funding databases was consuming significant staff time and still missing opportunities.
  • Building the system internally meant standing up an AI engineering function for a single capability, a multi-quarter, multi-headcount investment that wasn't justified.
  • MisaLabs was brought in as the implementation partner. The engagement began with focused working sessions. Within three weeks the scope, monthly engagement structure, and target production date were all agreed.

The impact

  • A capability their customers had been requesting for years went live in production within weeks of contract signature, opening a new revenue-generating service offering from day one.
  • The internal team stayed focused on their domain, researchers, content, customer relationships, while MisaLabs continues to operate and evolve the system on an ongoing monthly subscription.

Frequently asked questions

It depends on the scope of the use case and the size of the dedicated team, but the model is always a fixed monthly subscription, no six-figure upfront, no per-seat surprises. We share a clear number after the scoping conversation, before any work begins. You cancel anytime if it's not delivering.
You can transition ownership on your own timeline. The platform is deployed within your environment, and we provide the documentation, operational guidance, and knowledge transfer needed for your team to manage it internally when the time is right. We're happy to continue supporting the system long term, but the operational model is designed to give your team flexibility and control.
We expect requirements to evolve over time as the platform is used in real operational environments. Routine refinements, workflow adjustments, and smaller enhancements are addressed through the ongoing engagement process. When larger changes or new requirements emerge, we review them collaboratively and align on scope, priorities, timing, and any additional work before moving forward.

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