Co‑Transform · Hyperion AI
Three tracks. Ten modules.
Delivered on-site and live-online across MENA, EU and CIS.
Yerevan, Armenia
Plan a first cohortThe problem
Teams can understand the material and still struggle to apply it when the examples, tools, and success measures are disconnected from their day-to-day work.
Co‑Transform links learning to the systems people use, the decisions they own, and the evidence the organisation needs.
Standard courses use public datasets and simplified examples. Those are useful for teaching concepts, but they rarely reflect the repositories, data rules, and approval processes participants return to at work.
Teams need enough shared context to apply a method to their own systems, not just describe it in theory.
A prototype is only one part of an AI system. Deployment, evaluation, monitoring, cost, security, and failure recovery determine whether it can be used reliably.
Co‑Transform is led by practitioners who connect the concept to the operating decisions teams will face after the session.
A useful session can still have little effect when participants return without a suitable project, access to the right systems, or support from their manager.
The programme connects labs to current work and follows delivery with office hours, reviews, and an adoption plan.
Attendance and satisfaction help improve delivery, but they do not show whether a team can now perform the work.
We agree the evidence with the sponsor in advance, from module assessments to changes visible in repositories, delivery metrics, or operating practice.
Our standard
The programme connects role-specific learning, practical work, and follow-through. Six commitments hold every module to the same standard.
Practitioner-led. Modules are taught by people who have built and operated the systems they describe.
Production-first. Sessions cover deployment, cost, evaluation, monitoring, and recovery alongside model or application design.
Role-specific. Executives, engineers, and domain teams receive content shaped around the decisions each group owns.
Vendor-neutral. Tools are compared on evidence. No reseller agreement sits behind a recommendation. When the honest answer is “don’t use an LLM for this,” that is the answer given.
Hands-on by default. Most contact time is practical work, with labs adapted to the client’s stack and data where access and security allow.
Measured end to end. A skills baseline, module assessments, and a follow-up review show what participants learned and what changed in practice.
The architecture
The tracks run in parallel across different populations. They are not levels, and nobody graduates from one into the next.
For the people who approve the budget.
A focused track for the people responsible for investment, risk, organisation design, and vendor decisions. It gives leaders a common way to assess opportunities, decide what to build or buy, and set appropriate governance.
Starting with leadership gives the teams doing the work a clear mandate and decision path.
For the people who build the systems.
The core of the programme. This is where an engineering organisation acquires the ability to ship AI features and then keep them running — agent architectures, retrieval systems, observability, cost control, computer vision. Modules are independent enough to be taken singly and sequenced deliberately when taken together.
For everyone whose work is about to change.
A practical track for domain specialists in finance, legal, real estate, healthcare, government, and operations. Participants identify where AI can assist their work, where human review remains necessary, and which regulatory or policy constraints apply.
Module 07 sits in two tracks. For leadership it is a financial modelling session. For practitioners it is an engineering session. Same subject, different room.
The catalogue
Full outlines, expected outcomes, audience and format are available in the appendix below.
| # | Module | For | Format |
|---|---|---|---|
| 01 | Using Agentic AI for Coding | Software developers · Tech leads · Engineering managers | 1 day (6 hrs) |
| 02 | AI Transformation for Executives | C-suite · VP/Director level · Board members | Half day (3 hrs) · 1 hr keynote version |
| 03 | AI for Software Engineers | Mid-to-senior engineers · Backend and full-stack developers | 1–2 days · modular delivery available |
| 04 | RAG Systems: From Prototype to Production | ML engineers · AI developers · Backend engineers | 1 day (6 hrs) |
| 05 | AI Agents: From Prototype to Production | AI developers · Product engineers · Tech leads | 1 day (6 hrs) · 2-day version with extended labs |
| 06 | MLOps & AI Observability | ML engineers · Platform and DevOps engineers · Tech leads | 1 day (6 hrs) |
| 07 | LLM Cost Optimisation | Engineering leads · AI architects · CTOs · FinOps teams | Half day (3 hrs) |
| 08 | Computer Vision for Engineers | Software engineers · Data scientists · Embedded engineers | 1–2 days · 5-week bootcamp version available |
| 09 | AI for Domain Specialists | Domain professionals and their teams — content tailored per vertical | Half day to 1 day · customised per vertical |
| 10 | Managing Your Career in the Age of AI | All staff levels · Individual contributors · HR and L&D professionals | Half day (3 hrs) · 1 hr keynote version |
Four routes
We sequence by role. Four routes through the same ten modules, each ordered so that every module is useful before the next one begins.
02 → 07 → 10
Strategy first, because everything downstream depends on knowing which use cases are worth funding. Then cost, because the second question every board asks is what this will run to. Then the workforce conversation, because leadership has to answer it before staff ask.
Leaves with: A costed AI roadmap, a build-versus-buy position, and KPIs the CFO will accept.
01 → 03 → 05 → 06
Agentic coding tools come first — they change how the team works tomorrow, and they build appetite for everything after. Then the fundamentals of consuming AI in production. Then agents. Then the operations layer that keeps all of it alive.
Leaves with: Teams that ship and operate AI features end to end, without external help.
03 → 04 → 05 → 06 → 07
Retrieval before agents, because most agent failures are retrieval failures wearing a costume. Observability before cost, because you cannot optimise spend you cannot see.
Leaves with: Production RAG and agent systems, with evaluation suites, tracing and cost guardrails.
09 → 10
Opportunity mapping first, tailored to the vertical. Then the personal conversation about what changes for the individual, which is the one people are actually worried about.
Leaves with: A signed-off opportunity map, and staff who use AI safely inside regulation.
Five phases
Every engagement runs through five phases, and two of them happen before anyone opens a slide. A full-track engagement typically runs sixteen to twenty-six weeks end to end.
Interviews with the sponsor and two or three team leads. A skills baseline survey across the population. An honest review of the current stack, the data estate, and the use cases already on someone’s wish list. Most of what matters is decided here. A programme designed without this phase is a guess.
Output: A capability gap report.
Modules chosen. Pathways mapped per population. Labs rebuilt on the client’s own repositories and datasets, so that what gets built in the room is something the team can keep.
Output: An agreed curriculum and schedule.
Cohorts run on-site or live-online. Mostly hands, on real problems.
Output: Trained teams and working code.
Office hours. Code-review clinics. Internal champions identified and enabled, then handed the material. This phase deliberately overlaps delivery. Capability decays when the gap between learning something and applying it runs past two weeks, so we close that gap while the cohort is still running.
Output: Internal playbooks and champions.
Post-module assessment. A ninety-day behaviour review with line managers. A business metric readout against the baseline agreed in phase one.
Output: An ROI report the sponsor can present.
How a day runs
A standard day is six contact hours. Half-day formats compress the two lab blocks.
The problem restated in the client’s own systems, vocabulary and constraints. Thirty minutes. Nobody learns anything they cannot locate in their own work.
The minimum theory required to build. No more. Worked examples rather than survey slides.
Built together, step by step, everyone on the same commit. The instructor’s screen is the source of truth and the pace is set by the slowest person who is still trying.
Teams apply the pattern to their own case. The instructor circulates. Blockers are debugged live, in front of everyone, because the debugging is usually more instructive than the building.
Failure modes. A production checklist. Each participant writes a thirty-day plan for their own team before leaving the room.
Sixty percent of contact time is their hands on their own systems, not ours on a slide.
Every participant leaves with a running repository of everything they built, a production checklist for the pattern taught, and a written thirty-day plan. Session recordings and materials are licensed for unlimited internal reuse.
Four levels of evidence
Instruments and baselines are agreed before the first cohort so sponsors can distinguish participation from practical adoption.
Clarity, relevance and pace, scored per session and per instructor.
Used to improve delivery; reported separately from learning and business outcomes.
Movement against each stated module objective, measured before and after.
Adoption in real work. Agent-assisted pull requests. Evaluation suites running in CI. Cost dashboards live and watched.
Shows whether the learning has become part of normal work.
Delivery cycle time. Incident rate. Inference spend. Capacity released.
The relevant measures are selected with the sponsor during diagnosis.
The first year
An illustrative shape for a mid-size engineering organisation. Sequencing is adjusted per client — the order matters more than the dates.
Executives set direction. Engineering gets a skills baseline. Use cases are ranked by value, feasibility and risk. The KPI baseline is agreed with the sponsor.
Milestone: A signed roadmap.
The practitioner core runs. The first cohort ships something real. Office hours begin. Cost guardrails are defined before spend becomes a surprise.
Milestone: Two pilots in production.
Specialist modules land. Domain teams enter through module 09. Evaluation suites and cost controls reach CI. Observability rolls out.
Milestone: Systems you can trust.
Train-the-trainer. Internal champions certified. Module 10 delivered organisation-wide. ROI readout to the board.
Milestone: Capability you own.
The sequence keeps leadership decisions, technical learning, and operating changes connected throughout the programme.
Where and how
Consecutive days at your offices. Suited to intensive cohorts and work involving systems or data that cannot leave your network.
Weekly sessions across time zones on a shared lab environment. Built for distributed teams and multi-country rollouts.
Live core sessions plus recorded modules for asynchronous populations, licensed for unlimited internal reuse.
Track record
One hundred and eighty of them in assessed, project-based, multi-week cohorts. Two thousand more in talks and workshops.
The Computer Vision Bootcamp and Lyra AI Launchpad each ran for five cycles, allowing the material to improve through repeated delivery and assessment.
Audiences
Leadership teams, engineering organisations and regulated-industry operators working through procurement, governance and production constraints.
University students, professional diploma cohorts and intensive technical bootcamps assessed through projects rather than attendance.
Developer communities, technical conferences and practitioner meetups where the material is challenged by people who build and operate systems for a living.
The delivery format changes by audience; the emphasis on practical work, assessment, and clear outcomes stays consistent.
Three ways in
A single module delivered to one team, with an assessment at the end.
Best for: Proving fit before committing a budget.
Pathway design, labs rebuilt on your stack, cohort delivery, and a measured readout.
Best for: A team that already has a mandate.
The roadmap, every track, train-the-trainer handover, internal champions, and quarterly ROI readouts.
Best for: Owning the capability instead of renting it.
Commercial terms are quoted per engagement, once scope and cohort sizes are agreed.
Next steps
Appendix
Full outline, expected outcomes, audience and format for each of the ten modules. Open any module to see the complete brief.
From autocomplete to autonomous task execution.
A practical introduction to using coding agents as a supervised part of engineering work. The session covers where they help, where review is essential, and how to introduce them without weakening quality controls.
Strategy, governance and leading change at the top.
A focused session for the decisions leaders own: assessing readiness, prioritising use cases, choosing what to build or buy, structuring the team, evaluating vendors, and setting governance.
Production-grade skills for the AI-augmented engineer.
The foundation module for Track B. Everything an engineer needs to consume AI in production and nothing they do not — token economics, streaming and retries, prompting treated as a software craft rather than an art, and the failure modes that only appear at scale.
Retrieval-augmented generation that actually ships.
Most retrieval systems demo well and disappoint in production. This day is about the gap: chunking strategies chosen per document type, embedding models selected on evidence, hybrid search and reranking, and an evaluation suite that turns “it feels better” into a number.
Agentic systems that are reliable, cheap, and observable.
Agents fail in ways ordinary software does not. The day covers architecture selection, tool contracts an agent will actually honour, layered memory, checkpointing for partial failure, and the cost guardrails without which an agentic system is a standing invoice.
Ship AI to production and keep it there reliably.
The module that decides whether everything else survives. An honest maturity self-assessment, then instrumentation, service level objectives defined for AI rather than borrowed from web services, CI/CD with automated rollback, and incident runbooks written before they are needed.
Understand, control, and reduce the cost of production AI.
Delivered twice, to two audiences. For leadership it is a financial modelling session: a twelve-month projection, cloud versus on-premises TCO, and where the money actually goes. For engineers it is caching, routing, compression, batching — and an honest account of when not to use an LLM at all.
From fundamentals to production-grade CV pipelines.
The deepest module in the catalogue, drawn from a bootcamp that has run five cycles. Detection through deployment, YOLO and Vision Transformers compared on evidence, synthetic data for annotation scarcity, and the twelve ways CV projects die in production.
Finance · Legal · Real Estate · Healthcare · Government.
No transformers, no gradients. A structured method for translating a domain workflow into ranked AI opportunities, prompt design that produces auditable output, the regulatory constraints of the sector in the room, and a clear line between what is safe to automate and what needs a human.
Reskilling, positioning and staying relevant as AI advances.
The conversation staff are already having privately, held properly instead. An evidence-based map of which roles AI replaces, augments and creates; where upskilling time compounds versus commoditises; and for HR, a framework for designing reskilling at organisational scale.