Implementation & Integration
We write the code, handle the integrations, and hand off a system running in production — not a demo.
The Problem
Proofs of concept work in isolation. They fail when they hit real systems — messy data, legacy APIs, compliance requirements, and users who weren't involved in building it. Most AI implementation failures aren't technical. They happen because the system wasn't built for production from the start.
Common Pain Points:
POCs fail to scale to production environments
Integration with legacy systems proves more complex than anticipated
Security, compliance, and governance requirements not addressed in pilots
Lack of internal expertise to build and maintain AI systems
Underestimated operational complexity and ongoing maintenance
How We Build
We build with your real data from week one, involve your engineers throughout, and hand off a system they understand and can operate — not a black box with a manual.
Start with a working prototype on real data
The first 4–6 weeks produce a prototype running against your actual inputs — not synthetic examples. If the approach won't hold up, we find out here before committing to a full build.
Build for production from the start
Once validated, we architect the full system with monitoring, error handling, retry logic, and logging built in. We don't retrofit these later when something breaks.
Connect to your systems incrementally
We integrate with your APIs and data pipelines one step at a time, validating each connection before moving forward. Smaller surface area for things to go wrong.
Security and compliance in the design phase
We review data flows, access controls, and audit requirements with your security team before writing production code — not after.
Code reviews and handoff with your engineers
Your engineers attend reviews and architecture walkthroughs throughout the build. The documentation covers how the system works and how to extend it — so they can operate it without us.
What You'll Receive
Production-Ready AI System
Fully functional, tested AI solution integrated with your existing systems and ready for production deployment.
Technical Architecture Documentation
Detailed system architecture, data flows, API specifications, and integration points.
Security & Compliance Certification
Security assessment, compliance documentation, and penetration testing results.
Operational Runbooks
Procedures for monitoring, incident response, backup/recovery, and routine maintenance.
Automated Test Suite
Comprehensive automated tests including unit, integration, and end-to-end scenarios.
Team Training & Handoff
Training sessions and knowledge transfer to ensure your team can operate and maintain the solution.
Our Process
A proven 5-phase methodology that minimizes risk while maximizing impact
Discovery
2 weeksWe interview the teams doing the actual work, map your data, and find where AI can realistically help — and where it can't.
Strategy
2 weeksWe rank use cases by impact and build complexity, cut the list to what's worth doing, and produce a concrete plan for the first thing to build.
Prototype
4-6 weeksWe build a working prototype against your real data. If the approach won't hold up, we find out here — before committing to a full build.
Production
8-12 weeksWe build the full system, connect it to your stack, test against real edge cases, and hand off with your engineers trained to operate it.
Optimize
OngoingWe monitor output quality, tune as patterns change, and run quarterly reviews to decide what to improve next.
Engagement Options
Flexible models designed to fit your needs and constraints
90-Day POC to Production
Rapid implementation of a focused use case from concept to production deployment.
Full-Scale Implementation
Comprehensive implementation of complex AI systems with multiple integrations.
Implementation + Support
Implementation engagement with ongoing support and optimization for 12 months.
Ready to Get Started?
Let's discuss how implementation & integration can transform your organization.