AI as an engineering discipline
We don't ship vibes. Our systems are versioned, evaluated against real data, and transparent about their limits.
Measured, reproducible, swappable
Versioned everything
Models, prompts, and configs are versioned like code. Every result can be traced back to exactly what produced it.
Swappable backends
We keep clean interfaces between your product, its data, and the models that power it — hosted, on-prem, or hybrid — so you can change providers without rewriting the system.
Evaluated before promotion
Candidate models are scored against labeled data. Nothing reaches production unless it measurably beats what's already there.
Structured & honest output
Schema-enforced results with confidence and clear limits, so downstream systems — and people — know what they can rely on.
Proven defaults, adapted to you
We have strong opinions about engineering discipline — not about locking you into one cloud or one language. React dashboards, polyglot backends, models in the middle, data on your lakehouse, deployed on your infrastructure.
Next.js + React
Dashboards and web apps that are fast, accessible, and easy to extend.
C#, Python, or C++
Backend services matched to your team, performance needs, and existing code.
AI models
Hosted and local models behind one provider-agnostic interface.
Lakehouse-ready data
Delta, Iceberg, or your warehouse — versioned datasets with provenance for eval and production.
Your infrastructure
Azure, AWS, GCP, on-prem, or hybrid. We deploy where you already operate.
Spec-driven CI
Every feature ships from a spec, with tests and a passing pipeline.
Built for your environment — not a vendor roadmap
Cloud providers ship managed AI on their schedule. Databricks, open models, and on-prem stacks often expose capabilities months earlier — or in regions and compliance profiles the hyperscalers never will. We design so you can use the best tool available today and change your mind tomorrow.
Dashboards & interfaces
We build operator-facing dashboards and customer-facing apps in Next.js and React — or integrate into the front end you already run. The goal is clarity: real-time status, eval results, and human-in-the-loop review without a rip-and-replace.
Backends that match your shop
Need tight integration with a .NET estate? Python for ML pipelines? C++ for latency-sensitive inference? We pick the language and runtime that fits your team and constraints — not the other way around.
Lakehouse & data platforms
Many customers already live on Databricks, Snowflake, Fabric, or an open lakehouse stack. We architect around that: bronze/silver/gold layers, feature stores, batch and streaming feeds — so AI outputs land where your analysts and downstream systems expect them.
On-prem & self-hosted AI
Managed cloud AI services often lag behind what Databricks, open models, or the latest language runtimes expose. When that gap matters, we help you stand up LangChain, LlamaIndex, or custom agent stacks on your hardware — with the same evaluation and observability discipline we use in the cloud.
No vendor lock-in
Portable containers, open table formats, and provider-agnostic abstractions mean you are not stuck when a cloud SKU changes, a region lacks a feature, or pricing shifts. Swap inference providers, move workloads, or bring a component in-house without starting over.
Meet you where you are
Some engagements are greenfield. Most are not. We map to your VPC, identity provider, CI/CD, and compliance boundaries — then deliver incremental value on top of what you already trust.
Example: LangChain on-prem
A customer needs agent workflows with a model family their cloud provider does not offer yet, inside a network that cannot call external APIs. We containerize inference, wire LangChain (or your preferred orchestration layer) to internal data sources, add structured outputs and eval hooks, and ship a dashboard in React so operators can review and override — the same product quality as a fully managed cloud build, without waiting for a SKU or surrendering control.