Opportunity assessment
Your workflows scored for AI impact, feasibility and risk — grounded in what production AI can actually do.
AI Engineering
AI Strategy & Consulting
We help you choose the right use cases, architecture, and delivery plan so AI investment turns into production outcomes — not pilot graveyards.
- 2–4 wksto a costed, sequenced AI roadmap
- 3–5×typical ROI on first-wave use cases
- 0slideware — every recommendation is buildable
Capabilities
Strategy written by people who ship
Enterprise AI roadmaps, opportunity assessment, and prioritization for CTOs, CIOs and AI leaders.
Architecture and build-vs-buy
Reference architecture and honest vendor guidance from a team with no resale incentives.
Roadmap and sequencing
A phased plan that funds itself — early wins financing the platform investments.
Governance and readiness
Risk frameworks, talent plans and operating models that survive contact with legal and security.
How it works
From ambition to an executable plan
Most AI strategies fail at the handoff to engineering. Ours are written by engineers — every recommendation comes with an architecture and a cost estimate.
Assessment
Find the value, ignore the hype
We interview your teams, analyze your workflows and data, and score opportunities on impact, feasibility and risk — separating the transformative from the merely demoable.
- Workflow-level opportunity scoring with ROI models
- Data and integration readiness checks per use case
- Risk and compliance constraints mapped early
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Architecture
A blueprint engineering can build from
Reference architecture for your context: model strategy, retrieval, integration, governance and platform choices — with build-vs-buy calls justified in writing.
- Target architecture with phased migration path
- Vendor and model selection with eval evidence
- Platform investments sequenced against use-case ROI
Shared stateHandoffsRetriesTraces
Execution bridge
A roadmap that survives quarter one
Sequenced delivery plan with team shapes, budgets and success metrics — and if you want, we build the first wave ourselves to prove the plan at production quality.
- Quarter-by-quarter plan with costed initiatives
- KPIs and eval criteria defined before building starts
- Optional delivery of wave one by our engineering team
Invoice exception workflowRunning
✓Extract invoice fields1.1s
✓Match against PO0.6s
Resolve mismatch with agentrunning
·Post to ERPqueued
Use cases
Where ai strategy & consulting delivers value
Enterprise AI roadmaps
Board-ready strategy grounded in engineering reality.
AI portfolio rescue
Stalled initiatives triaged: fix, merge or stop — with evidence.
Build-vs-buy decisions
Honest evaluation of vendors against building on your own stack.
AI governance design
Risk, compliance and approval frameworks that don't strangle delivery.
Due diligence
Technical assessment of AI claims in targets and vendors.
Executive enablement
Leadership workshops that turn AI literacy into decisions.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Every consultancy showed us a maturity matrix. This was the first team that showed us an architecture, a cost model and then built the first use case themselves in ten weeks.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- Anthropic Claude
- OpenAI
- AWS
- Azure
- Google Cloud
- Databricks
- Snowflake
- LangGraph
- MCP
- Terraform
- Kubernetes
- Datadog
FAQ
Common questions
How is this different from a big-firm AI strategy?
It's written by engineers who will be accountable for building it. Every recommendation has an architecture, cost estimate and risk assessment behind it — and we're willing to deliver wave one, which keeps the strategy honest.
How long does an engagement take?
A focused assessment and roadmap takes two to four weeks. Larger scopes — multi-division, heavy compliance — run six to eight. You get findings weekly, not a big reveal at the end.
We already have ideas. Do we still need this?
Ideas usually aren't the problem — sequencing, feasibility and platform decisions are. We pressure-test your list, kill the traps, and order the survivors so early wins fund the platform.
Do you recommend specific vendors?
Yes, with reasoning and no resale margin. Where evidence is thin, we run short evals on your data rather than trusting benchmark marketing.
Next step
Get a plan you can actually build
Two weeks, your data, our engineers — walk away with a roadmap the board and the builders both believe.