Frontier models, engineered into production.
From autonomous agents to generative AI strategy and MLOps, we build AI systems that ship, stay governed in production, and hold up under real load — the same agent architectures we run on our own operations. Measurable outcomes, not slideware.
What we build
Agentic AI Development
We build autonomous agents that reason, plan, and act across multi-step business workflows — not chatbots that stall at the first ambiguity. Built on Claude and orchestrated with LangChain, LangGraph, or CrewAI, our agents connect to your real systems and APIs over the Model Context Protocol — with explicit boundaries on what each agent may read and write, and evals measuring behavior continuously, so autonomy moves work forward across finance, operations, customer service, and R&D without becoming unaccountable. Agentic AI runs inside our own platforms in production, not on a slide: a company-wide work-orchestration MCP server on ClickVSCode coordinates mission boards and daily operations across the portfolio, an agentic HR platform runs onboarding and compliance autonomously with human judgment kept for decisions, and a self-evolving software platform adapts its own code as the business it serves changes. When we design your agent architecture, it is one we already operate ourselves.
Generative AI Consulting
The frontier moves every quarter; your strategy has to keep pace without chasing hype. We audit your processes, rank use cases by value and feasibility, design responsible-AI governance, and hand you a roadmap that turns GenAI spend from an open-ended experiment into a governed investment tied to strategic goals — from first proof-of-concept to a production rollout your organization can actually operate.
MLOps & Evals
The gap between a demo and a dependable system is operations. We stand up end-to-end pipelines for training, versioning, CI/CD, and monitoring, and we wire in rigorous evals so quality is measured, not assumed. Track drift, latency, and cost with full observability, and ship model changes with the same confidence you ship code.
LLM Integration
We embed large language models directly into your products and internal tools — retrieval-augmented generation over your own data, targeted fine-tuning, disciplined prompt engineering, and secure API integration with Claude, GPT-5-class models, Gemini, and strong open-weight alternatives — with the model layer kept swappable, so routine calls can route to cheaper models and no single vendor's cost curve locks you in. Every integration is built for enterprise security, low latency, and predictable throughput.
Enterprise AI Transformation
Point solutions don't compound; an operating model does. We partner with leadership to stand up AI centers of excellence, redesign processes around AI-first principles, upskill teams, and build the data foundation that keeps innovation continuous rather than one-off. The outcome is durable capability your organization owns.
AI that navigates the physical world
Most AI lives in a datacenter. Ours also runs on the device — right-sized models on the microcontroller, reading sensors and driving actuators in a closed loop. The same team writes the firmware and the model, so there is no seam between the silicon and the intelligence.
Edge inference on device
Right-sized models — quantized and pruned to fit MCU and embedded targets, RP2040/ESP32-class parts and edge accelerators — so inference runs where the sensor is. No round trip to the cloud in the control loop, no dependency on a network that might not be there.
Sensor → model → actuator loops
We close the perception-to-action loop in firmware: sensors feed the on-device model, the model decides, and actuators respond in real time. Latency, determinism, and failure behavior are engineered against the real silicon, not assumed from a datasheet.
One team, silicon to cloud
The engineers who build the model also write the firmware it runs on and the backend it reports to. There is no seam between an ML shop, a firmware contractor, and an app agency — because it is one team from silicon to cloud, accountable end to end.
Models ship like firmware
New models reach deployed hardware over the same OTA pipeline that carries firmware, so a fleet in the field keeps getting smarter long after it leaves the bench. Versioning, rollback, and eval gates apply to the model the same way they apply to code.
How we work
- 01
Discovery & AI Readiness
We audit your data assets, infrastructure, workflows, and organizational appetite to gauge real AI readiness. That means naming the high-impact use cases, quantifying likely ROI, and surfacing the data-quality and governance gaps that have to close before any model ships.
- 02
Architecture & Prototyping
Our architects design a scalable, secure solution tuned to your environment, then build rapid prototypes to validate assumptions and prove business value early. Stakeholders see and shape the system before full engineering begins — no surprises at the finish line.
- 03
Build, Integrate & Test
We build production-grade systems with unit, integration, and adversarial testing for reliability and safety, and integrate cleanly with your ERP, CRM, data warehouse, or custom APIs. Evals run continuously so quality is a number we watch, not a hope we hold.
- 04
Deploy & Improve
We ship with full observability — model performance, data drift, latency, and business KPIs all instrumented. After launch we own the monitoring, retraining cadence, and iterative improvements that keep the system accurate as your business shifts underneath it.
Spec-driven, AI-assisted, human-gated.
We build client software the way we advise clients to adopt AI: a structured pipeline where AI accelerates the work and humans gate it. Every delivery passes two human review gates, an AI-driven penetration test, automated code scanning, and manual testing before it ships.
- 01
Structured Specification
Our developers do not vibe-code applications. Before AI writes a line, our business intelligence team converts requirements into structured specs — data models, acceptance criteria, and the success metrics the delivery will be measured against.
- 02
AI-Assisted Development
Engineers build with AI against the spec. AI provides the velocity; the spec provides the direction; the engineer stays accountable for every line that lands.
- 03
Human Code Review
Senior engineers review every AI-assisted change. Nothing merges on the AI's word alone — architecture, correctness, and maintainability are judged by a person whose name goes on the review.
- 04
AI Security Pass
AI-driven penetration testing probes the running application while automated code scanning sweeps the codebase — injection, auth, secrets, and dependency risks surfaced before a human ever signs off.
- 05
Human Verification & Manual Testing
A second human review works through everything the security pass flagged, and manual functional testing exercises the product the way a real user will. Machine findings end with human judgment.
- 06
Delivery, Measured
The build ships when both the AI gates and the human gates pass. Outcomes are measured against the spec's own success metrics — quality, timeline, and ROI made visible instead of asserted.
The tooling
Common questions
RFI-001How long does an AI pilot take to reach production?
RFI-002Do we need a large proprietary dataset to get started?
RFI-003How do you handle the security and privacy of our data?
RFI-004How is an agentic system different from traditional automation?
RFI-005How do you keep autonomous agents governed in production?
RFI-006What does an engagement cost?
