Big AI built for big players.
We wrap your AI — models, generative AI, or agents — in the security, governance, scale, and operations an enterprise demands, so it reaches governed production instead of stalling in pilot.
Security & Access Control
Single sign-on (SSO via SAML or OIDC), role-based access control, encryption in transit and at rest, and managed secrets — built to your enterprise security baseline so the AI system passes InfoSec review instead of stalling in it.
Data Governance & Privacy
PII is identified and handled at the data layer, with access boundaries, data lineage, and retention controls — so the AI uses your data within the governance rules your organization already enforces, not around them.
Scalable, Cloud-Native Architecture
Containerized, horizontally scalable services on your cloud (Azure, AWS, or GCP), with separate development, staging, and production environments — architected for real organizational load, not a single-user demo.
Built to Fit Your Estate
Delivered behind versioned APIs and standard, supported interfaces so the AI runs alongside the systems your teams already use — without brittle, one-off connections that break on the next upgrade. Exact systems and interfaces are scoped per engagement.
Observability & Operations
Monitoring, quality and drift checks, audit logging, and alerting — with the dashboards and on-call runbooks your operations team needs to run the system after launch, so day-2 is designed for, not discovered in an incident.
Adoption & Handoff
Rollout planning, role-based onboarding, documentation, and structured knowledge transfer — so the AI is actually adopted across the organization and your team can operate and extend it without staying dependent on us.
The Enterprise Delivery Wrapper
The model is the easy part.
A working model is the center, not the finish line. What carries it across the pilot-to-production gap is everything wrapped around it — and that wrapper is what we build.
Governed enterprise production
Click any layer to see what we build and what your team receives.
Why pilots stall — and why this one won't.
Enterprise AI rarely fails because the model is wrong. It fails because the wrapper around it was never built: it couldn't pass InfoSec review, couldn't scale past the demo, broke a governance rule, or had no plan for the day after launch.
Each layer here is one of those gates. We build them in from the first sprint so the AI reaches production — and stays there.
Governed enterprise production — outer to core
Tap any layer to see what we build and what your team receives.
Out of Pilot Purgatory
AI built to clear the security, architecture, and governance gates that strand most enterprise pilots — so the initiative reaches production instead of dying as another impressive demo nobody could ship.
Passes Enterprise Review
Designed around your security baseline, access model, and data-governance rules from the first sprint, so review becomes a checkpoint you pass — not a wall you hit the week before launch.
Scales Past the Demo
Architected for real organizational load across proper environments, so the system that works for ten internal users keeps working when the whole organization depends on it.
No Vendor Lock-In
Built on open, portable frameworks on your own cloud, with documentation, environments, and structured handoff so your team can run, update, and extend the system independently of us.
Operable After Launch
Monitoring, audit logging, alerting, and runbooks built in, so day-2 operations are engineered up front — your team runs the system from a dashboard, not from a production fire.
Key Capabilities
- Enterprise AI architecture and solution design
- Security and access control (SSO via SAML/OIDC, RBAC)
- Data governance, PII handling, lineage, and access boundaries
- Cloud-native, containerized, horizontally scalable services
- Multi-environment delivery (dev / staging / prod) and CI/CD
- Versioned API delivery and standards-based interfacing
- Observability: monitoring, evaluation, drift and quality checks
- Audit logging, alerting, and operational dashboards
- Runbooks, environment documentation, and rollback procedures
- Adoption planning, role-based onboarding, and knowledge transfer
Technologies
Engagement Models
Enterprise AI Readiness Sprint
- Current-state and security/governance review of the target use case
- Reference architecture for enterprise-grade delivery
- Risk, data, and access assessment with a clear scope boundary
- Production-readiness roadmap with timeline and estimate
Enterprise AI Build
- Solution architecture and security design
- The AI capability built — model, generative AI, or agent (scoped with you)
- Access control, data-governance controls, and environments
- Observability, audit logging, and operational runbooks
- Deployment and structured handoff to your team
Enterprise AI Build + Managed Operations
- Everything in Enterprise AI Build
- Ongoing monitoring, evaluation, and incident response
- Scheduled updates, tuning, and dependency maintenance
- Periodic security and data-governance reviews
- Direct Slack/Teams channel with the delivery team
Frequently Asked Questions
How do you keep an AI project from stalling in pilot purgatory?
We design for the enterprise gates from day one instead of bolting them on at the end. The readiness sprint surfaces the security, architecture, and data-governance requirements your initiative will be judged against, and the build is structured to satisfy them as it goes — access control, environments, observability, and handoff are part of the work, not afterthoughts. Most pilots die not because the model is bad but because nobody engineered the wrapper around it that production demands. Closing that gap is the entire focus of the engagement.
Can the AI pass our security and architecture review?
That is the design target. We build to your enterprise security baseline — single sign-on through SAML or OIDC, role-based access control, encryption in transit and at rest, and managed secrets — and deliver across proper development, staging, and production environments with CI/CD. We document the architecture for your reviewers rather than reconstructing it under pressure. We cannot approve our own work through your governance process, but we build so that review is a checkpoint you pass, and we work directly with your security and architecture teams to get there.
Will it work alongside our existing enterprise systems?
We build the AI to run alongside your estate through standard, supported interfaces — typically versioned APIs your systems can call — rather than brittle, one-off connections that break on the next upgrade. The exact systems and interfaces in scope are defined together during the readiness sprint, so the plan reflects your real environment. To be clear about scope: we do not claim turnkey, pre-built connectors to any specific third-party platform. We engineer to the documented, supported interfaces your systems expose, and we scope that work explicitly before we commit to it.
How do you handle data governance and PII?
PII is identified at the data layer and handled according to your governance requirements — masking, exclusion, or restricted access depending on what the use case actually needs. We build in access boundaries, data lineage, and retention controls so there is a traceable record of what data the AI can reach and why, which supports obligations like CCPA and your internal data-governance program. We architect privacy decisions to be explicit and documented rather than buried in code. We do not provide legal compliance certification; we build the system so it supports your governance program rather than working against it.
Do we own the system, or are we locked into you to keep it running?
You own it. We build on open, portable frameworks deployed on your own cloud, with a documented architecture, environments, audit logging, and operational runbooks. Handoff includes structured knowledge transfer so your engineers can run, update, and extend the system without us. If you choose the managed operations option, that is a convenience and not a dependency — you can take operations in-house at any point, and the documentation is written so you can.
What if our AI initiative is still just a pilot or an idea?
Start with the Enterprise AI Readiness Sprint. In two to three weeks we review the use case against your security and governance requirements, produce a reference architecture for enterprise delivery, and hand you a production-readiness roadmap with a realistic estimate and a clear scope boundary. Sometimes the outcome is that the use case is ready to build, and sometimes it is that the data or access model needs work first — and saying so is a legitimate result. You leave with a credible plan to reach production, not a six-figure commitment made on a hunch.
Ready to take AI from pilot to governed production?
Book a 30-minute call. We will discuss your use case, your enterprise requirements, and whether a readiness sprint or a full build is the right place to start.