Enterprise AI Enablement · Towards AI
Enterprise AI enablement

We teach you to build relevant AI tools.

We help enterprise teams move from AI pilots to custom production-ready deployments.

Book a consultationBook a consultationSee how we engageSee how we engage
Trusted by leading enterprises
and institutions
J.P. MorganIntelEuropolVeloce IT
The gap we fix

AI fluency gaps are why most pilots never reach production.

95%

of enterprise generative AI pilots fail to produce measurable P&L impact.

MIT Sloan, 2025
5%

of employees use AI in ways that meaningfully transform their work — despite 88% of organisations reporting AI adoption.

EY Work Reimagined Survey, 15,000 employees, 2025
25% → 76%

AI adoption jumps from 25% to 76% when employers provide structured AI training.

Bright Horizons, 2025
60%

of AI projects are unsupported by AI-ready data, driving low adoption.

Gartner, 2025–26

Nine reasons AI pilots don’t reach production.

After six years of building AI systems and training enterprise teams, we see the same pattern.

01

Wrong team lead

Picking classic software or ML leaders instead of people with LLM and domain depth.

02

Weak domain expertise integration

End-users are often not properly embedded in workflow design.

03

“On-prem only” reflex

Privacy and cost fears push teams onto small local language models that simply cannot do the job.

04

Optimising for cheap, not smart

Adoption happens at capability thresholds. A better model saves more human rework than it costs.

05

Assuming staff “just get” AI

Most people need ~20 hours of applied, hands-on training before they start to build safe, effective AI intuition.

06

Workflow bolt-on

Tools that do not reshape work produce no measurable P&L impact. High performers substantially redesign workflows.

07

Demo-to-production gap

Single-turn happy-path demos hide that multi-step agents are distributed systems where every tool call can fail.

08

Evals and validation gap

Best-in-class AI teams spend more time on eval infrastructure than on model selection.

09

Best methods stay private inside leading labs

By the time they hit blogs or open-source, you are a release cycle behind. You need teams generating novel approaches in-house.

Sound familiar? Let’s talk about your bottlenecks.

Book a consultationarrow_forward
How we work

Buy before build. Pods, not bodies.

We start with a buy-before-build ladder: an existing product plus training comes first, and custom software has to earn its place through reliability, integration, control, or unit cost. That discipline keeps budgets on the problems where bespoke engineering pays.

When a build clears that bar, it runs through a small engineering pod: an AI engineer and a full-stack engineer paired with a deployment strategist, with architect support across pods. The pod that ships your system also trains the team that will own it.

Every build follows one engineering rule.

Code for rules. Models for meaning. People for responsibility.

Four ways to partner with us.

Pick the engagement that matches where your team is. Hover any phase to see what's inside.

Advise

Prioritise use-cases by ROI, buy-vs-build, and system architecture.

Model selection and roadmaps that survive operating scrutiny. Helps leadership avoid the costly early mistakes that kill pilots.

  • Use-case prioritisation tied to commercial impact
  • Buy-vs-build, model selection, system architecture
  • Roadmaps that hold up under operating scrutiny
Training delivery

Three delivery intensities.

One training system, delivered at the intensity your team needs.

Per-seat licence

Role-based learning paths for hundreds to thousands of seats.

The full course bundle with role-based paths, a live kickoff, monthly mentorship clinics, private cohort channels, and leadership analytics. Lifetime updates, because model guidance goes stale within months.

  • Role-based paths across technical and non-technical teams
  • Live kickoff, monthly clinics, private cohort channels
  • Leadership analytics and lifetime content updates
Beyond training

An AI Excellence partner, on both sides of the build.

01

We guide

AI strategy and standards: approved tools, review conventions, evaluation gates, and a prioritised use-case portfolio owned at leadership level.

02

We build

Custom AI systems designed, shipped, and transferred by the same engineers who teach.

03

We lead

Fractional Chief AI Officer, offered as Fractional AI Leadership where the title sits better beside an existing CIO or CTO.

Training is the entry point. We stay on as a long-term AI deployment partner.

Working with us

How an engagement runs.

01

Scoping call, 60 minutes

With the instructors who deliver the programme. There is no separate sales team.

02

Paid co-design

A nominee per priority team works with us to shape modules, exercises, and success measures.

03

Pilot cohort

Typically 20 to 40 engineers. Real exercises from your roadmap, delivered on-site or remote, in English or French.

04

Certification and debrief

Certification against shipped capstones plus a written capability debrief per cohort.

05

We scale to further waves only when the debrief earns it.

Procurement-ready: NDAs, PO and invoicing supported. No production access required. Your code stays yours.

Technical team

Best-in-class AI practitioners, distributed globally.

Deployment strategists, forward-deployed engineers, and leaders from Palantir, BCG, UBS, J.P. Morgan, Aurelius Growth Capital, and MILA, working with AI engineers we trained in our own community.

01🇨🇦
Canada
AI Solutions Engineer
LLM apps and integration
02🇨🇦
Canada
NLP Doctorate
LLM researcher
03🇮🇳
India
Applied AI Engineer
Agents specialist
04🇨🇦
Canada
Full-stack Engineer
LLM integrator
05🇩🇪
Germany
Full-stack Engineer
LLM developer
06🇨🇦
Canada
MILA-trained Engineer
Retrieval-augmented generation
07🇨🇦
Canada
LLM Eval Engineer
Fine-tuning specialist
08🇲🇾
Malaysia
AI Engineer
Broad AI practitioner
09🇺🇸
United States
AI PhD
Foundational models
10🇨🇦
Canada
AI Engineer
MLOps, CV, NLP
11🇮🇳
India
AI Engineer
Applied AI development
12
And many more
Joining through our global community
Case studies

From non-technical to AI-powered operator.

We upskill, advise & build. Recent examples of our engagements.

Enterprise trainingParis

Upskilling Europol’s special agents to use state-of-art AI tools to automate intelligence workflows.

Client

Europol — the law enforcement organisation that supports complex investigations and intelligence work across Europe.

What we delivered
  • Multi-day intensive bootcamp in Paris on advanced LLM pipelines, prompt engineering, and fine-tuning agents.
  • Tailored to mission-critical law-enforcement workflows: multi-modal scraping, coded-language and slang identification.
  • Secure deployment patterns for sensitive intelligence environments.
Leading PE Fund
Custom AI deploymentUSA

Automating job-cost reporting for an HVAC services buy & build.

Client

A leading PE fund’s HVAC services platform. Workers logged material costs per job on paper and backoffice re-typed them into Excel, growing days sales outstanding and weakening audit trails.

What we delivered
  • AI pipeline that reads PDFs of paper receipts.
  • Automatic extraction and calculation of material costs per job.
  • Real-time consolidation and job-level reporting.
  • Audit-ready monthly reports, available instantly rather than at month-end.
UK IT Services Group
Custom AI deploymentUnited Kingdom

Tender discovery and prioritisation for an IT services buy & build.

Client

A UK IT services group. Team members spent hours daily scanning fragmented tender sites across many geographies; high-value tenders were easy to miss and hard to prioritise.

What we delivered
  • Automated scraping across 5+ tender platforms.
  • AI ranking by relevance to the group’s strengths and categorisation of likely win-rate.
  • A daily report of newly-relevant opportunities, recovering many analyst hours per week.
Nviya-Prime
Agentic financial intelligenceEurope

Building an agentic engine that reproduces banker reasoning to identify what to pitch, to whom, and when.

Client

Nviya-Prime, an agentic B2B financial-technology platform developed for a UK startup founded by a former banking partner.

What we delivered
  • An agentic intelligence platform combining filings, live news, risk data and future-event signals into prioritised, product-specific actions for bankers.
  • Deterministic workflows and autonomous agents spanning three source tiers, with around 800 prompts and one million words of research per company.
  • Modular, compliance-led deployment across Gemini, OpenAI, Claude and Grok, supporting client-cloud, API-only and locally hosted configurations.
Book a consultationarrow_forward

Tell us what your team needs to learn, improve, or build.

We’ll help you prioritise the right use cases, train the right people, and ship the right systems. Every enquiry is read by the Towards AI leadership team.

Book a consultationarrow_forwardEmail us

Reply within one business day.