Strategenix AI Studio
Where we build what we recommend.
The Strategenix AI Studio is our applied AI build unit. It takes a priority use case from hypothesis to working software in fixed-length sprints, measures it against explicit criteria, and returns a clear recommendation: stop, refine, pilot, or scale. The people who framed the strategy sit with the people writing the code, so the prototype answers the questions the investment committee will ask.
What the Studio is
Advise and build, with the same team, without a handoff
Most consultancies advise. Most integrators build. The Studio exists so that Strategenix does both with the same team, on the same decision, without a handoff.
The Studio is a standing, cross-disciplinary team: product designers, AI engineers, data engineers, security engineers, and architects, led by a partner who is accountable for the outcome. It works in fixed-length sprints with fixed outputs. It builds three kinds of things: client solutions, from prototype to production increment; reusable assets that every subsequent engagement inherits; and Strategenix products, starting with SHAIDE Hunt.
What the Studio is not
Four things the Studio is not
- Not a research lab. Every sprint starts with a business decision and ends with a recommendation on it.
- Not a staffing pool. Studio teams are assembled around the problem, run by a partner, and priced by the sprint, not by the hour.
- Not a demo factory. A prototype that cannot be evaluated, secured, and operated is not finished.
- Not a vendor showroom. Platform and model choices are made on the evidence for your use case.
How it fits
The practices decide and govern. The Studio proves and builds.
| The practices | The Studio |
|---|---|
| Set the AI strategy and choose the portfolio | Take the top use cases to working prototypes |
| Define the target architecture and standards | Prove the architecture with real data, integrations, and load |
| Design governance, risk classification, and controls | Enforce the controls in code and produce the evidence |
| Assess readiness across data, skills, and operations | Build the pilot, the operating model, and the handoff |
| Advise the executive team on the decision | Return the evidence the decision needs |
An engagement can start in either place. A strategy engagement that identifies three priority use cases hands them to the Studio. A Studio sprint that reveals a data or governance gap hands it back to the practice. The same partner stays accountable across both.
Engagement formats
Fixed scope, fixed output
Discovery Sprint
Frame the use case before choosing technology: the user, the decision, the workflow, the value hypothesis, the risk boundary, the data need, and the success measure. Ends with a go or no-go on a Prototype Sprint.
Prototype Sprint
Build the smallest working experience that tests the critical assumptions, then evaluate it against explicit criteria for quality, safety, latency, cost, user behavior, and control effectiveness.
Pilot and Industrialization
Turn a validated prototype into a controlled pilot or production increment with ownership, controls, monitoring, support, and measurement defined, and with your team ready to run it.
Co-Development
Studio engineers and architects work inside your team for a defined period to build a capability together and leave the skills behind.
Joint Innovation
Work with your leaders and domain experts to develop new products, services, or operating models that use AI responsibly, from concept through the first customers.
The evaluation standard
A prototype is not finished until it has been measured
Every Studio prototype ships with an evaluation report. The report states the test set, the criteria, the results, the failure modes, and the limits of what was tested. It is written for the people who will fund the next step, not for the people who built the prototype.
- Quality: accuracy, groundedness, and consistency against a defined test set
- Safety: harmful output, policy violations, prompt injection, and data leakage
- Human oversight: where a person decides, and whether the handoff works
- Performance and cost: latency, throughput, and cost per transaction at expected volume
- Adoption: whether the intended users trusted and used the workflow
- Control effectiveness: whether the required controls can be enforced and evidenced
What the Studio builds
Representative solution types
- Enterprise copilots and knowledge assistants over governed content
- Agentic workflows that retrieve, reason, use tools, and route decisions to people
- Document, case, and claims intelligence for regulated processes
- Decision-support and analytics applications with AI-assisted interpretation
- Security investigation, evidence, and compliance tooling, including SHAIDE Hunt
- Evaluation harnesses and guardrail services that other AI systems run behind
Studio assets
What every engagement inherits
The Studio turns what it learns into assets that every subsequent engagement inherits: reference architectures for retrieval and agents, an evaluation harness, security guardrail patterns, governance intake and risk-classification templates, and deployment patterns for the major cloud platforms. Clients receive the assets used in their engagement as part of the work.
Outcomes
What clients receive
- A defined use case and measurable value hypothesis
- A working prototype or pilot appropriate to the decision
- Evaluation results, assumptions, limitations, and risk findings
- A reference architecture and control model
- A production backlog, roadmap, operating model, and cost view
- A clear recommendation to stop, refine, pilot, or scale
Product incubation
In development with design partnersSHAIDE Hunt: security investigation, evidence, and compliance reporting in one workspace
SHAIDE Hunt is the Studio’s first product: an AI-assisted platform that helps security teams organize signals, triage against policy, build investigation timelines, preserve evidence with integrity controls, and produce control-mapped reports across the tools they already use. It came out of the same discipline the Studio applies to client work: frame the decision, build the smallest useful system, measure it, and industrialize what holds up. It is in development with design partners, and we are selective about who joins that group.
Signal intake
Bring relevant alerts and context together from endpoint, identity, cloud, SaaS, network, and security platforms.
Policy-aware triage
Prioritize events against defined policy, recommend next actions, and route decisions for human approval.
Investigation workspace
A structured case timeline with connected artifacts, analyst actions, approvals, and decision history.
Evidence preservation
Capture artifacts with integrity checks, time references, access controls, and retention appropriate to the investigation.
Compliance reporting
Map investigation activity and evidence to relevant control requirements and produce review-ready reports.
Test the decision before you make the investment.
Bring the Studio a use case, a workflow, or a product idea. We will define what has to be true, build the evidence, and tell you what we found, including when the answer is stop.