Specialized
AI that works in your business, not just in a demo
We build the data systems and infrastructure your AI needs to run reliably at scale. We always fix the data foundation first — because that's where most AI projects fail.
Data first
Foundation before model development
Governed
Audit trails and model cards included
Access
controlled retrieval in every RAG system
AI Ops
Retraining and drift monitoring built in
What we build
End-to-end AI infrastructure for regulated environments
Enterprise AI deployments fail for predictable reasons: bad data, no governance, and missing operational infrastructure. We address all three before the model is deployed.
Data infrastructure for AI
Most AI projects fail before the AI even runs — because the data feeding it is messy, poorly organized, or untracked. We build the data pipelines and storage your AI needs to work reliably. We fix the data foundation before touching the AI model.
Includes: Data pipelines, feature storage, data lineage, quality checks
Deploying and running AI models
We put AI models into production — with version control, the ability to roll back, testing infrastructure, and monitoring that catches when a model starts behaving differently from how it was designed.
Works with: Kubernetes, SageMaker, Vertex AI, Azure ML, self-hosted
AI that searches your internal documents
We build systems that let your AI search and answer questions from your organization's own documents and data — with the same access controls your team already uses. Not everyone should see every document.
Includes: Search infrastructure design, document chunking, access control integration
AI oversight and governance
We set up the oversight frameworks that regulated industries need for AI — audit trails, bias checks, explanations of how decisions were made, and the documentation regulators ask for. Built before launch, not after something goes wrong.
Frameworks: EU AI Act, NIST AI RMF, industry-specific requirements
AI security
We secure your AI systems against the specific ways they can be attacked — including manipulating the prompts fed to them, extracting private data through their outputs, and tricking them into doing things they shouldn't. AI has different vulnerabilities than regular software, and we design for them.
Controls: Prompt manipulation prevention, output filtering, private data detection
AI operations and maintenance
We build the systems that keep your AI models working correctly over time — automated retraining, validation checks before new models go live, and monitoring that catches when a model's outputs start drifting from what's expected.
Includes: Training pipelines, validation checks, drift monitoring, rollback capability
Technology
The full AI stack — from data to production monitoring
We work with all major AI and ML platforms. Technology selection follows your performance requirements, compliance constraints, and existing infrastructure — not our vendor preferences.
Data & Features
The foundation every model depends on
Model Training
Framework-agnostic; we work with your existing tooling
Model Serving
Optimized for your latency and cost targets
LLM & RAG
With access control enforced at the retrieval layer
AI Operations & Pipelines
Version-controlled, automated, auditable
Monitoring & Governance
Drift detection, bias monitoring, audit trails
How we work
From data readiness to production AI
Most AI programs start with the model and work backwards. We start with the data infrastructure, build to production quality, then develop the model on a foundation that can support it.
AI readiness assessment
Week 1–2
We look at your data, your existing tools, and how your organization works today — and identify the real blockers before any AI work starts. Usually those blockers are data quality problems, missing infrastructure, or governance gaps.
AI readiness report + list of blockers + recommended scope
Data and infrastructure foundation
Week 2–8
We build the data infrastructure your AI needs — pipelines, storage, data lineage tracking, and access controls. We only start working on AI models after we're confident the data feeding them is reliable.
Working data foundation + data pipeline + quality checks
Building the first use case
Week 6–14
We build, test, and deploy the first AI feature to a staging environment. We define how to measure whether it's working correctly and prepare the documentation needed before production launch.
Tested AI model + evaluation criteria + governance documentation
Production launch
Week 12–18
The AI goes live with monitoring, alerts, and a tested rollback procedure. We document every step so your team owns the process — not just us.
Live AI model + monitoring dashboard + rollback procedure
Handoff to your team
Week 16–20
We set up automated retraining, drift monitoring, and the day-to-day operational process your team will run. We train your team on how to maintain it, read the monitoring data, and escalate when something looks wrong.
Automated retraining + monitoring guide + team training
Use Cases
AI problems we're built to solve
Financial Services
Deploying AI credit risk models in a regulated environment
The Situation
A bank wants to use AI to evaluate credit risk. The models work in research settings, but the underlying data is spread across three systems with inconsistent formatting and no tracking of where data came from. The compliance team won't approve anything for production unless every AI decision can be fully explained.
Our Approach
We built the data infrastructure first — organizing, connecting, and tracking all the data sources before touching the model. The deployed model includes a layer that generates a clear, structured explanation for every decision it makes. The compliance team received a full documentation package and audit trail before the production approval review.
Healthcare
Building an internal AI assistant that respects patient privacy
The Situation
A hospital wants to give clinical staff an AI assistant that can search internal documents and answer questions. The challenge: a nurse shouldn't be able to access records outside their department, and the system needs to meet HIPAA requirements for privacy and auditability.
Our Approach
We built the search system so access controls are enforced before any documents are ever retrieved — the AI never sees records the user isn't authorized to access. Every search and retrieval is logged to an audit trail that satisfies HIPAA requirements for breach investigations.
Is this right for you?
This is a good fit if you…
- You want AI working in actual business operations — not a demo or a side project
- You have large volumes of internal data that AI could work with, if it were properly organized
- You're spending money on AI tools and getting unreliable or inconsistent results
- Leadership is being asked about AI strategy and needs a credible, deliverable answer
- You're in a regulated industry where AI decisions need to be explainable and auditable
You might want to start elsewhere if…
- You need a consumer chatbot or basic automation — that's a much simpler project
- You want to run a quick experiment with no commitment to production — start smaller
Common questions
Questions people ask before getting started
Plain answers. No jargon. If something isn't covered here, just ask us directly.
Ready to talk enterprise AI?
Tell us what you're trying to build and where your data estate stands today. We'll assess readiness and scope an engagement that addresses the real blockers.