Wrong use cases
Topics chosen without real impact, or too complex to implement.
Our method
From an AI idea to a service your teams actually use.
Most AI projects die between the demo and real usage. Here is how we close that gap: what blocks it, our three-step method, the engagement path, and what we commit to.
01 The reality
What companies lack isn't the idea. It's the integration.
An AI use case only creates value when it's chosen with the teams, connected to the right tools, secured, adopted and measured over time.
Insufficient integration
AI disconnected from existing data, tools and processes.
Weak adoption
A solution delivered, but rarely used by teams day to day.
02 Our method
Equip, observe, automate.
Everything starts from the business and the data, never from the technology: a brilliant agent wired to an unprepared information system reasons poorly. We start by understanding what matters to the teams, then we only automate what they have validated in practice.
Phase 01 / 03
Equip the humans
We first put AI in the hands of your teams, on their real tools and their real day-to-day cases.
Phase 02 / 03
Observe what works
Real usage surfaces the use cases genuinely worth automating: the ones that recur and create value.
Phase 03 / 03
Automate what repeats
Each agent earns its autonomy against explicit criteria: recurrence, determinism, reversibility, observability and team consensus. Nothing ships to production without control.
03 Engagement
End-to-end support, in three steps.
A path tailored to your maturity. We start at the most useful step: scope, deploy or scale.
01 Entry point 1
Scoping
Explore & prioritise
Identify the right AI use cases and choose priorities.
Includes
- Workshops with business and IT teams
- Use-case qualification
- Impact / feasibility / risk scoring
- Execution roadmap and budget estimate
02 Entry point 2
Deployment
Build & integrate
Turn a priority use case into a service your teams can actually use.
Includes
- Functional and technical design
- Integration with existing tools and data
- Business copilot, specialised agent or automation
- Access, traceability, training and production rollout
03 Entry point 3
Run & adoption
Extend & maintain
Sustain, measure and scale the first use cases.
Includes
- Usage / cost / quality monitoring
- Continuous improvement
- Training of internal champions
- Extension to other teams and new uses
One trajectory, one person accountable. We don't sell days; we deliver a bounded outcome: clear scope, defined deliverables, measurable adoption.
04 Our commitments
A clear frame, before the first line of code.
A bounded result, not billed days
Written scope, identified deliverables, adoption criteria agreed with you before we start. You know what you are buying.
Foundations that stay yours
Code, infrastructure and documentation live in your own accounts. Every engagement leaves a reusable base, not a dependency.
Control designed in, not bolted on
Access, traceability, sensitive data and human oversight are handled during framing, never added afterwards.
05 Frequently asked
What decision-makers ask us.
A few answers to frame a first conversation.
What is Intelligence Partners?
An AI integration firm that helps companies take their AI use cases from concept to an operational service. You get a senior Forward Deployed AI Engineer (FDE) as your single point of contact, accountable for the result, backed by an ecosystem of experts: AI consultants and developers, IT integration, cloud, DevOps, security, data and training.
Who do you work with?
Mid-market companies and large accounts that want to industrialise AI. Our natural counterparts are CIOs, CTOs, executive, product and business leadership.
How are you different from an IT services company, a freelancer or a strategy firm?
We sell neither days nor slides. We deliver a bounded outcome, with an accountable senior lead and an ecosystem of experts mobilised as needed. And we go all the way to production: every mission also lays a reusable foundation, so the next project plugs in faster than the last.
Do you only work on AWS?
AWS is where we excel and we're an Anthropic partner. But we start from the client's real environment. Our role is to integrate AI into your information system, not to impose a technology.
Does it fit if we already have a cloud, data or AI team?
Yes. In that case we come in to accelerate: we help scope, prioritise, secure and deliver in collaboration with your internal teams.
How does a mission start?
Most often with a short scoping: objectives, use cases, available data, security constraints, existing architecture, risks, expected value and roadmap. It lets you decide what to build, in what order and with what investment.
How do you measure success?
With simple indicators defined from the start: real usage, time saved, quality, reduced risk, controlled costs and repeatability. AI that nobody uses isn't a success.