SprintiQ™Advise.
Strategic advisory for leaders deploying AI in real environments. Architecture, vendor strategy, program design, and governance: the decisions that compound for years. Plus enterprise training when the team needs durable practice, not a slide deck.
You don't need a builder yet. You need a clear-eyed read.
Most organizations standing up AI programs face the same decisions before any code is written: which architecture fits the problem, which vendors deliver versus pitch, what success looks like, how to staff and budget, and where the program lands in the organization.
Getting these wrong is expensive. Architecture decisions made in the first sprint compound for two years. Vendor relationships locked in early are hard to unwind. Programs without a clear success definition drift until someone cancels them.
SprintiQ advisory makes these decisions with you, delivered by a working AI product founder making the same calls in production, not a consultant assembling a deck from someone else's playbook.
Six recurring
decision areas.
Most engagements concentrate on one or two. The trade-offs are real, not religious, and easier to get right with someone who has made them before.
Architecture selection
RAG versus fine-tuning versus agents versus traditional ML. Which pattern fits the problem, and which is fashionable but wrong for your case.
Model & vendor strategy
Which foundation models, which infrastructure providers, which evaluation tooling, which observability stack. Opinions backed by production experience, not vendor relationships.
Build versus buy
Where to invest in proprietary capability versus integrate someone else's. Most organizations get this exactly backwards.
Program design & staffing
What roles to hire, in what order, with what mandate. Where AI lives organizationally: engineering, product, a standalone function, or distributed.
Evaluation & success metrics
What "working" actually means for your specific use case, and how to measure it before users tell you it isn't.
Risk posture & governance
What your AI program actually needs from a risk, legal, and compliance standpoint, versus what consultants will try to sell you.
Four formats.
Sized to the decision.
Advisory scales from a focused two-week intensive to an ongoing retained relationship. Custom engagements where industry-specific compliance, security, or domain depth shapes scope.
Strategy intensive
A focused engagement around a specific decision. Discovery, options, recommendation, written assessment. For leaders who need one call made well.
Quarterly advisor
Retained advisory for organizations standing up or scaling AI programs. Regular working sessions, ad-hoc consultation, board-level briefings.
Leadership workshop
Working sessions for leadership teams who need to align on AI strategy and operating cadence before committing budget.
Enterprise training
When the decision is made and the team needs durable capability. Cohort-based curriculum, detailed below. Often paired with an advisory retainer for the leaders.
Enterprise Training
Most AI training doesn't stick: slide decks, certificates, a hackathon, then back to work where nothing changed. Ours is cohort-based, grounded in cognitive science, and structured so every participant ships real work and leaves with capability they can use Monday morning.
Finding the gap, making your first build
For teams that have used AI tools as consumers but never shipped real product with them. Identifying problems worth solving and shipping something small and real.
Building in small bets
AI assistants over-build by default. The decomposition discipline that separates a working prototype from a working product, and reading the signal once you ship.
AI coding tools at real quality
Tool selection, verification discipline, context orchestration, and the security basics every builder needs before showing their work to anyone.
Does it work? Does anyone want it?
Talking to real users instead of running surveys. Telling genuine interest from polite interest. Deciding whether to continue, pivot, or stop on evidence.
The Playbook Prompt Library
Most training produces a certificate. Ours produces a working artifact: a personal build playbook each participant generates from their own work, captured as a structured prompt library that runs against any modern AI tool.
It's portable, tool-agnostic, and belongs to the participant. By the end it's fourteen sections deep, the most efficient way to onboard a teammate or train a junior engineer. The meta-thesis: we teach AI-assisted building using AI-assisted learning consolidation. The method demonstrates itself.
Grounded in cognitive science
The curriculum design is the credential. Three frameworks, applied at every layer:
- Andragogy. Adults learn what they need when they need it. Every module structures around a working artifact, not a quiz.
- Cognitive Load Theory. Working memory holds about seven items. Materials are engineered for managed load: short sequences, progressive disclosure.
- Skill consolidation. Real capability comes from spaced practice and real outputs. We design for what the brain actually does.
Start with a call.
Advisory begins with a working call to define the decision you need help making. For training, a 30-minute scoping call. We propose a fit, or tell you we're not the right partner.