Data readiness
Where the relevant data lives, its structure and quality, how permissions are modeled, and whether retrieval against it can meet an accuracy bar.
AI Readiness Assessment
A structured evaluation of your data, infrastructure, security constraints, and candidate use cases — delivered as a prioritized roadmap with a recommended deployment architecture.
Most organizations do not have an AI strategy problem. They have an unresolved question about what is actually feasible given their data, their infrastructure, and their regulatory position — and every proposal they receive assumes that question away.
The assessment answers it. You get a clear picture of which use cases are viable, which are not and why, what deployment model your constraints permit, what it will cost to build and to run, and what has to be true before anything reaches production.
Scope
What we evaluate
Infrastructure & capacity
Existing compute, GPU availability or procurement path, network topology, Kubernetes footprint, and what a deployment would actually run on.
Security & regulatory constraints
Which frameworks apply, what your security organization requires, and which deployment models are approvable in your environment.
Integration surface
The systems of record a useful system would have to read from and write back to, and what those integrations realistically cost.
Use case inventory
Candidate use cases collected, scored against feasibility and value, and sequenced — including the ones we recommend against.
Team capability
Who would operate the system after go-live, what skills exist internally, and what the gap implies for staffing or managed operations.
Deliverables
What you receive
- Readiness report — current state across data, infrastructure, security, and capability, with the specific blockers named.
- Prioritized use case roadmap — scored, sequenced, and explicit about what we recommend not doing.
- Recommended reference architecture — a real diagram, sized to your environment.
- Deployment model recommendation — private cloud, on-premise, edge, air-gapped, or managed API, with TCO modeling for the realistic options.
- Security and compliance considerations — what your review will require and what evidence has to exist.
- Sequenced implementation plan — phases, dependencies, and what has to be true before production.
Process
How it works
01
Scoping call
45 minutes to understand your environment, constraints, and what triggered the work. We confirm fit and scope before anything is committed.
02
Discovery
Working sessions with your data, infrastructure, security, and business stakeholders. Typically a handful of sessions across two to three weeks, structured around your people's availability rather than ours.
03
Analysis and architecture
We model deployment options against your constraints, score use cases, and design the recommended architecture.
04
Readout
A working session with your team, plus an executive readout written for the people who approve budget. You keep every artifact.
Fixed fee, time-boxed. The assessment is scoped and priced before it starts, so there is no open-ended discovery engagement. We quote after the scoping call, once we know the size of your environment.
After the assessment
The roadmap is yours.
There is no obligation to engage us for implementation. The roadmap and architecture are written to be executable by another firm or by your own team, and they are yours to use that way.
We work this way because an assessment that only makes sense as a sales document is not worth buying — and because it is, in practice, how we win the follow-on work.
Request
Request an AI Readiness Assessment
Tell us where you are. An engineer reads every submission — this is not a sales queue.
Email
hello@sigmaticlabs.io
Response
Within one business day
Confidentiality
We will sign your NDA before a detailed technical discussion.
Security correspondence
security@sigmaticlabs.io