We solve the problems that stall your business.

Your systems weren’t designed to talk to each other. But your business needs them to. You’ve got data trapped in silos, manual processes bridging the gaps, and teams re-entering the same information across multiple platforms.

We connect systems - modern APIs, legacy constraints, SOAP, REST, proprietary interfaces, direct database integrations, file-based transfers, and scenarios where the best move is building a unified interface over existing applications rather than forcing a rip-and-replace.

We’ve done this at scale inside some of the most demanding technical environments in the world - including building a personalized content delivery platform for Google’s cloud sales organization that integrated massive volumes of product documentation and delivered custom, client-specific feeds to sales reps.

We’re also one of the few firms with deep, certified FHIR expertise. We use FHIR as the universal standard for healthcare data exchange and couple it with AI to dynamically map non-FHIR data into compliant resources - no manual field mapping, no interoperability consultants. Our team includes HL7 FHIR-certified developers at multiple certification levels. For healthcare organizations, this means integrations that aren’t just connected - they’re interoperable at scale.

Integration work is where a lot of firms either oversimplify the problem or get lost in it. We’ve been doing this for over a decade. We know where the hard parts are before we start.

AI is everywhere. The difference is whether it changes how your team actually works.

We treat AI as a tool to remove drudgery and compress time-to-output - especially in workflows that used to require weeks of manual effort. We build AI into the core of how your systems work, not as a decoration on top.

That means getting your data ready first, understanding the workflows where AI can make a measurable difference, and then building implementations that your team actually adopts. We focus on practical automation that changes how people work day-to-day - not demos that impress a boardroom but never make it to production.

We don’t just build AI for clients. We run it internally. In late 2025, we introduced an autonomous AI agent as a new teammate. It has its own accounts, its own tasks, and it works alongside our human team and select customers. We didn’t announce that the new hire wasn’t a real person. We ran the experiment to find the rough edges of fully autonomous task execution, and we found them. The agent was leaking extra information in outbound communications, sharing details from internal calendars when scheduling meetings with clients. We built safeguards to review outbound communication and ensure it stays appropriate.

That kind of hard-won operational experience is what separates real AI implementation from demo-day theater. We’ve made the mistakes so you don’t have to.

Not every problem needs a large language model. We’ve deployed classical machine learning for healthcare data classification and processing, achieving throughput increases of over 300%. We’ve built LLM-powered products that handle complex domain-specific tasks through natural language. And we’re running autonomous agents that operate independently across real business processes. The right tool depends on the problem. We know when to use each one.

A concrete example: our DocSimplify platform uses conversational AI to configure healthcare systems and map data to FHIR - a process that used to take weeks of manual work by specialized consultants. Users describe what they want in plain language, the AI builds and configures it, automated agents test and validate the output, and the result deploys with one click. That’s what we mean by AI that changes workflows.