AI consulting that ends in shipped software
From strategy and use-case discovery to custom models, automation, integration and deployment — we take AI from idea to production on Azure, built for organizations whose data can't go to someone else's cloud.
The pilot works. Then it dies.
Industry research consistently finds that the large majority of enterprise AI initiatives never reach production. The model is rarely the problem — the operating model around it is.
Compliance veto
Compliance treated as a final checkpoint instead of a design constraint. GxP, HIPAA, and SOC 2 reviews kill the project at the finish line.
Security objection
Public network paths to AI services, API keys in code, no privileged identity management. InfoSec says no — correctly.
Cost surprise
The first production month arrives at several times the pilot estimate. No token budgeting, no caching, no model-tier strategy.
Quality drift
Output quality degrades and nobody notices, because no evaluation harness was ever built.
Ownership vacuum
The pilot team moves on. No runbook, no on-call, no budget owner — so the workload quietly rots.
Eleven ways we put AI to work
Start with strategy and let it scope the rest, or come in at the one you need. Every engagement is fixed-scope and ends with something you own.
AI strategy
Where AI fits in your business and what to build first.
Use-case discovery
Finding the high-value tasks worth automating or improving — and the ones that are not.
Process automation
Chatbots, workflows, document handling and data entry that run without a person in the loop.
Custom model development
Building or fine-tuning models for the needs an off-the-shelf model cannot meet.
Data prep and governance
Organizing your data, quality checks and access rules so AI has something trustworthy to work with.
Integration
Connecting AI with CRM, ERP, websites, APIs and internal systems so it changes what actually happens.
Analytics and forecasting
Predictions, dashboards and trend analysis your team can act on.
Prompt engineering
Designing prompts and systems that give reliable outputs, not lucky ones.
AI training
Teaching your teams how to use AI effectively — and where not to.
Risk and compliance
Security, bias checks, policy guidance and testing built in from the start.
Deployment and support
Rollout, monitoring, maintenance and iteration once it is live.
Three scoped engagements, not open-ended hours
Each one is fixed-scope with defined deliverables, and each naturally scopes the next. Start at the top — it's designed to be a low-risk way to find out whether we're the right partner.
AI Readiness Assessment
We score your organization against the AI Readiness Maturity Model, find what's actually blocking production, and hand you a prioritized roadmap.
- Stakeholder interviews and current-state assessment
- Maturity scoring across governance, observability, cost, ownership
- Compliance gap analysis for your regulatory context
- Target-state architecture recommendation
- Prioritized 90-day roadmap with effort estimates
Pilot-to-Production Sprint
Take one AI use case — new, or stalled — and put it into production properly: identity, private networking, evaluation, cost guardrails, and a runbook.
- Azure OpenAI deployment with Private Link, no public paths
- Managed Identity throughout — no API keys in code
- RAG pipeline: ingestion, chunking, hybrid retrieval, citations
- Automated evaluation harness with a labeled test set
- Token telemetry, cost guardrails, and content safety
- Runbook, on-call handoff, and team walkthrough
AI Platform Foundation
Build the platform layer so the next ten AI workloads take weeks instead of quarters — the move from one working thing to an operating capability.
- AI landing zone aligned to the Cloud Adoption Framework
- Shared platform components product teams can build on
- Policy-as-code guardrails and a governed model catalog
- FinOps baseline: tagging, per-team showback, budget alerts
- Everything as Bicep or Terraform — reproducible, no drift
- Enablement sessions and full knowledge transfer
What makes this different
Regulated by default
Two decades in pharma, healthcare, financial services, and insurance. GxP, HIPAA, SOC 2, and PCI DSS are design inputs here, not surprises at the end.
Your data stays yours
Private Link, Managed Identity, customer-managed keys, and — where it matters — fully local models. Nothing leaves your tenant that you didn't choose to send.
We ship, not just advise
We build and run production software ourselves. Every recommendation is one we've had to live with operationally.
Everything as code
Bicep, Terraform, policy-as-code. Reproducible environments, peer-reviewed infrastructure, and a clean handoff — you're never locked to us.
Read the thinking before you buy the work
The Enterprise AI Playbook is our 26-page field guide covering the maturity model, the Azure reference architecture, the compliance playbook, FinOps for AI workloads, and the 90-day roadmap. It's the same framework we run engagements on.
Start with a conversation, not a contract
Thirty minutes. Tell us about your environment, your compliance context, and what you're trying to achieve. We'll tell you honestly whether we're the right fit.
Book a call