Applied AI
AI work that reaches production and keeps working after the demo.
For teams that need real, working outcomes from AI. Bounded engagements, model-agnostic, discovery first.
ReasonPeak's Applied AI work turns a working AI pilot into a system your business runs on, with the operations, governance, and integration most teams skip after the demo. Engagements are bounded and model-agnostic: we choose the model that fits your problem, not a partner's. The work spans AI economics and model-mix architecture, and the discipline of AI-assisted delivery. Most engagements start with a two- to six-week AI Decision Sprint that ends in a written, prioritized call on where AI is worth it.
What you can count on
- EU-based, working US business hours
- Bounded engagements, milestone-based
- Owners involved in the work, not just the sale
- Discovery before delivery
- Deep domain roots in telecom and EV-network operations
Why do AI projects stall after the demo?
The technology usually works. What doesn't get built is everything around it: the operations, the governance, the integration into systems people already use. A pilot that impresses in a demo stalls on the way to production. That gap is the work we focus on. It is the implementation most consulting engagements skip, and it turns a model that works in principle into one your business can run on, measure, and trust.
What does Applied AI cover?
These disciplines run through every engagement:
Knowing where AI fits, and where it doesn't
We work from your problem, not from a model. AI earns its place where it genuinely pays off — and sometimes the honest call is to buy instead of build, or to leave a problem alone. Knowing what's worth building, and what isn't, comes before any of the building.
AI Economics & Model-Mix Architecture
Costing and architecting AI the way it will actually run. We right-size the model mix — larger models to coordinate, smaller ones for the narrow work — keep token costs in view, and design so you can swap models as the market moves. Vendor-neutral by construction, eval-driven throughout.
AI-Assisted SDLC Discipline & Governance
Using AI inside the build process, with the discipline to keep it trustworthy. Where agentic coding tools help, we put governance around them: attribution and audit trails for AI-generated code, review tooling in the pipeline, prompt and policy governance, and evals that catch regressions. AI in the loop — accountable by default.
Why bring this to ReasonPeak?
Production focus, not pilot focus
Most AI engagements stop at a demo. Ours are scoped around what gets shipped, used, and measured — the Sprint format is built to reach production, and the Adoption Program measures impact, not just deployment.
Owners involved, a scoped team that scales
The people who scope the work stay involved in delivering it — you work directly with them, not through layers of hand-offs. Engagements are scoped to the outcome, and the team scales with the work.
Model-agnostic, vendor-neutral
Leading commercial AI models and platforms (Claude, ChatGPT, Gemini), open-weight models (Mistral, Qwen, Muse Glimmer), and small language models or specialized machine learning where they fit the task — chosen by fit to your problem, not by a partner commitment. You get an independent read on the AI stack, and the freedom to change it as your needs and the market move.
How do engagements work, and where do I start?
AI Decision Sprint
Two to six weeks, a fixed fee scoped to your situation. Structured working sessions that end in a written, prioritized decision: where AI is worth it in your operation, what to build first, what to skip. Yours to keep and act on with anyone.
AI Build Sprint
Four to eight weeks, fixed scope. One concrete AI capability shipped to staging or production — a voice agent for ops calls, document-workflow automation, retrieval over internal knowledge, or an LLM integration into an existing product.
AI Adoption Program
Three to six months, phased. Discovery, several Build Sprints, a measurement framework, and adoption support — for when the value lives in changing how a function or team works, not in a single capability.
Does AI advice from a generalist actually fit my domain?
We apply AI where we have real operational ground to stand on — telecom subscriber and network operations, and EV charging networks — and we bring the same model-agnostic discipline to AI work outside those domains. Our team has designed LLM pipelines, evaluation harnesses, retrieval over internal knowledge, agent workflows, and AI copilots for customer-facing roles. These are the shape of work we have done hands-on. We lead with method, because that is what carries to your problem — how the work is reasoned, costed, and built — and a Decision Sprint puts exactly that on the table, in writing, for you to keep.
Questions teams ask before an AI engagement
How is this different from AI strategy consulting?
We build and advise. Strategy decks stop at recommendations; our engagements are scoped to produce a working system or a written decision you can act on. Most start with a two- to six-week AI Decision Sprint that ends in a prioritized build/buy/skip call you own, so you leave with something usable, not a slideshow.
Which AI models and vendors do you work with?
Whichever fits your problem. We work across the leading commercial AI models and platforms (Claude, ChatGPT, Gemini), open-weight models (Mistral, Qwen, Muse Glimmer), and small language models or specialized machine learning where they fit the task, and we often combine them — a larger model to coordinate, smaller ones for narrow tasks. We are not tied to any vendor's partner program, so the recommendation you get is based on your data, budget, and governance, not ours.
Do you use AI in your own delivery?
Yes — with discipline around it. Where AI helps us build faster, we keep attribution and audit trails for AI-generated code, run it through review tooling, and govern it with evals that catch regressions. We do not ship code straight from a model to production: AI stays in the loop, and people stay accountable for what ships.
We tried an AI pilot and it stalled. What would be different here?
We focus on the part that usually gets skipped. A pilot proves a model can work; reaching production means building the operations, integration, and governance around it. We scope engagements around what gets shipped, used, and measured — and if a Decision Sprint shows an idea isn't worth building, we tell you that before you spend on a build.
How do we start, and what does it cost?
Most engagements start with a two- to six-week AI Decision Sprint (at a fixed fee scoped to your situation) that ends in a written decision on where AI is worth it. Build Sprints and the Adoption Program are scoped and priced to the work that follows. If you are not sure which fits, a short scoping call is the best place to start.
Ready to find out where AI is actually worth it?
The first call is to understand fit and scope. You are not committing to a paid Sprint by booking the call.
Email us at team@reasonpeak.com.