AI & Machine Learning · CORTEX
AI Readiness Assessments, prioritized roadmaps, build-vs-buy guidance, and the operating cadence that turns AI ambition into production engineering — by the engineers who’ll ship the work, not by a strategy team that hands off.
The problem
The pattern that produces it: a board-level AI mandate; a strategy phase that produces use-case heat-maps and capability frameworks; a vendor-selection process that runs longer than most build phases; an engineering organization that wasn’t in the room and inherits an unbuildable plan; and a quarterly steering committee that watches the dashboard drift sideways for two years before someone quietly rescopes. The strategy isn’t wrong; the gap between strategy and engineering is.
Prosigns runs AI strategy as engineering-led. CORTEX consultants are the same engineers who ship CORTEX engagements — they scope what they’ll build, with the build phase staffed by the same team. Our AI Readiness Assessment, build-vs-buy guidance, and vendor-selection support are calibrated against shipping reality, not procurement frameworks. The output is the roadmap that actually gets built.
Where it ships
Specific applications we’ve built and operated. Not speculative — every example below is grounded in a real shipped engagement.
$50K – $150K
fixed-price
Two-week diagnostic across data, infrastructure, talent, governance, and use-case backlog. Output: scored report, prioritized roadmap, defensible budget. Same scoring methodology as our free /tools/ assessment, calibrated to your specific context.
Score candidate AI use cases on data availability, business impact, technical risk, governance complexity, and time-to-production. Defensible rubric the board can sign off on, not a vendor-driven heat-map.
Honest assessment per use case. Vendor platforms make sense for some; custom builds for others; partners (us or others) for the rest. We tell you when our own services aren’t the right answer.
Frontier vs mid-tier vs self-hosted. Specific vendor evaluation against your real workload using our eval methodology. Models change every quarter; the framework that picks them shouldn’t.
Model cards, eval harness, audit logging, vendor risk review process. Designed to satisfy your specific regulatory frame (SR 11-7, HIPAA, EU AI Act) — not a one-size governance overlay.
AI strategy support for boards and CIOs — quarterly reviews, capability assessments, talent planning. Engineer-led; not a slide-deck practice dressed up as advisory.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion. Not slogans — operating rules.
Senior CORTEX consultants run discovery — the same engineers who’d ship the build phase if we engaged on it. Output is calibrated to shipping reality, not procurement frameworks.
Use-case scoring uses a documented rubric rather than analyst-style heat-maps. Boards can sign off because the scoring is transparent; engineering teams can execute because the priorities map to real workload.
We turn down ~30% of inbound. AI consulting is the most common path where we recommend a different vendor, an internal hire, or no action at all. Saying no is part of the deliverable.
Roadmaps include explicit assumptions, dependencies, and skill requirements. We surface what your team can ship in-house vs what needs partner support — and what shouldn’t be attempted yet at all.
Capabilities
Stack
Selected work
Common questions
Different operating model. Big 4 strategy typically optimizes for the hand-off to a delivery partner; we optimize for the engineering reality of what gets built. Our consultants are senior engineers (G8+) who ship CORTEX engagements — not strategy specialists who hand off. The roadmap reflects what we’ll actually ship if you engage on the build phase.
Yes — frequently. Roughly 30% of AI consulting engagements end with us recommending a non-AI solution, a vendor platform, an internal hire, or no action at all. AI is the right answer for some workloads; rules engines, classical ML, or process redesign are right for others. The point is the assessment, not the AI deployment.
No. Discovery and consulting deliverables are yours to act on however you want — including taking them to a different vendor for delivery, building internally, or shelving the program. Many readiness engagements end with us recommending you build internally; we say so honestly.
Yes — EU AI Act risk classification and mapping to architectural components is part of our governance frame design. We’re tracking the regulatory rollout and can co-author your AI register, conformity assessments where required, and the post-market monitoring posture the Act calls for.
Engineer-led, quarterly cadence, calibrated to your specific committee structure (audit, risk, technology). We’ve supported board AI conversations at financial services, healthcare, and growth-stage SaaS clients. Honest framing of capability vs hype is what boards actually want — and rarely get from analyst-style advisory.
AI Readiness Assessment: 2–4 weeks, $50K–$150K. Use-case prioritization + roadmap: 4–8 weeks, $80K–$250K. Vendor selection (single-use-case eval): 4–6 weeks, $60K–$180K. AI governance frame build: 6–12 weeks, $120K–$400K. Quarterly board / executive advisory: $30K–$80K per engagement.
Within AI & Machine Learning
Talk to us
A senior engineer plus the CORTEX department lead joins the first call. No discovery gauntlet, no junior reps.
