Model & provider strategy
Choose models based on the task, quality, latency, privacy, and cost — without hard-wiring the product unnecessarily to one provider.
Enterprise AI & agentic systems
Build AI that does useful work in production.
We build AI products, enterprise agents, knowledge systems, and intelligent workflows around real business problems — with the data, evaluation, permissions, human control, and software engineering they need to hold up in production.
The model is only one part of the system.
A production AI system has to know what information it can use, which tools it can call, what a user is allowed to do, how quality is measured, when a human must approve an action, and what happens when the model is wrong.
We engineer the whole system around the model.
What we build.
AI products
AI capabilities embedded inside software people already use — copilots, decision support, document intelligence, recommendations, multimodal workflows, and contextual assistance.
Enterprise agents
Systems that can reason over business context, use approved tools, call APIs, complete multi-step work, and request human approval when needed.
Knowledge systems
Search and reasoning across documents, databases, applications, and internal knowledge — with permissions, provenance, and source inspection.
Intelligent automation
Document processing, operations workflows, analysis, triage, extraction, classification, support, and other high-volume work where AI can reduce manual effort.
AI platforms
Shared model access, evaluation, observability, governance, permissions, and cost controls behind multiple AI applications.
Production AI needs explicit boundaries.
The architecture should make it clear what the model knows, what it can do, what policy applies, how quality is checked, and where people remain responsible.
- MODEL
- CONTEXT
- TOOLS
- POLICY
- EVALUATION
- HUMAN APPROVAL WHEN REQUIRED
- ACTION
- AUDIT
What we engineer around the model.
Retrieval & context
Ground the system in the information it actually needs, with permissions and provenance preserved.
Tools & actions
Give agents bounded access to APIs and systems instead of unrestricted access to the business.
Evaluation
Define task-specific tests, representative datasets, and acceptable failure thresholds before production.
Security & permissions
Protect sensitive context, constrain tools, validate outputs, and keep agent authority explicit.
Observability & economics
Monitor quality, failures, latency, model usage, and operating cost after the system ships.
Production AI needs tests, not opinions.
AI is only an asset once it's in production and trusted.
A model in a notebook changes nothing. We focus on getting AI into the product safely — where it actually moves the business.
Cross the demo-to-production gap
Turn a promising prototype into a feature real users can rely on.
Build on data you can trust
The pipelines and structure that make AI outputs dependable.
Ship AI you can defend
Validation and audit trails that stand up to scrutiny.
Keep humans in control
Guardrails and review where the stakes are too high for autonomy.
Fast doesn't have to mean unpredictable.
The reason software runs late and over budget is rarely the code — it's the chaos around it. We plan before we build, run the work as Missions you can watch, validate every change against an explicit definition of done, and review it before it ships. You get the speed of AI with the discipline that keeps software maintainable for years.
Proven on software companies actually depend on.
14 years building production software for
Proof — Commerce Data Science
40M
products indexed
We built Commerce Data Science a platform that crawls hundreds of retail sites into a single 40-million-product index.
Commerce Data Science needed to turn the open web into structured commerce data. We built a crawling-and-extraction platform that pulls hundreds of retail sites into a single 40-million-product index — the kind of data foundation real AI and analytics features are built on. It's data engineering at scale, and it's the honest substrate under credible enterprise AI.
A 14-year playbook, enforced by software.
Across more than 200 products, the difference between projects that ship clean and projects that spiral came down to a few disciplines. We turned them into how Cortex runs every build — so they happen every time, not just when someone remembers.
Plan in Missions
Every build starts as a plan a human approves — scope, definition of done, and boundaries — before a line of code is written.
Trace every commit
Each change is tied back to the plan it came from, so you can always see what was built, why, and by whom.
Test the contract
Work is validated against an explicit definition of done, not a demo — proven correct before it ever ships.
From COBOL to serverless.
We build on a modern stack and we read the old one before we replace it. Over a decade across enterprises means we're as comfortable in a legacy monolith as we are on the edge.
Modern stack
- React & TypeScript
- Node.js, Python & Go
- Serverless & edge
- Cloud-native (AWS / GCP / Azure)
- Postgres & modern data stores
- AI / LLM application layers
Legacy we speak
- COBOL & mainframe logic
- Java & .NET monoliths
- Legacy relational databases
- On-prem & regulated environments
- Undocumented internal APIs
- Systems no one wants to touch
Who it's for.
Teams with high-value workflows that are still manual
Use AI where understanding, classification, generation, or multi-step coordination is consuming human capacity.
Product companies adding AI to software users already depend on
Introduce AI without weakening reliability, permissions, usability, or trust.
Enterprises with knowledge spread across systems
Create governed ways for users and agents to work across information that is currently fragmented.
Teams with a strong AI prototype that is stuck
Close the gap between a convincing demo and a production system.
Companies exploring agents
Identify which actions can safely be delegated, where approvals belong, and how agent behaviour will be evaluated.
Not a typical software company.
Forward-deployed engineers, not juniors
Every engagement is led by engineers who've shipped production software for years.
AI speed, with the controls
Validation and human review are built into the process, not bolted on at the end.
Fully yours, fully accountable
Full IP assignment, an NDA from the first conversation, and an audit trail that outlives the project.
Questions, answered.
What kinds of enterprise AI systems do you build?
We build AI capabilities inside products, enterprise agents, knowledge and retrieval systems, document and workflow automation, multimodal applications, and the shared AI platform infrastructure behind them. The exact architecture depends on the business problem, the available data, the reliability required, and how much authority the system should have.
Our AI works in a demo but not in production. Can you help?
That's the most common problem we see, and the one we're built for. The gap is usually data quality, evaluation, and reliability — not the model. We engineer those so the feature holds up with real users.
How do you make sure AI features are safe and accountable?
We set explicit accuracy and safety bars, validate against them, keep a human in the loop where the stakes are high, and record an audit trail — so behavior is provable, not guessed at.
Do you have enterprise AI case studies?
We're candid here: our deepest published proof is in data platforms and product engineering at scale, like a 40-million-product commerce index, rather than a single headline AI metric. For AI work we lead with our delivery method and the closest real builds — and we won't overstate what we haven't shipped.
Start with a clear-eyed look at your build.
Whether you're starting fresh or improving something that exists, begin with a free Agent Readiness Assessment — a concrete read on where AI-assisted delivery is safe, where it's risky, and where the leverage is. The report is yours either way.
This service is available as a Cortex Pod — scoped to an outcome, governed end to end.