Wrong use cases
Topics chosen without real impact, or too complex to implement.
AI embedded in your company, serving your business.
Our AI consultants ship your artificial intelligence solutions where the work actually happens: in your tools, your processes, your teams.
Already trusting us
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 Use cases
Concrete AI use cases, integrated into your operations.
Intelligence Partners helps companies turn AI into operational use cases, integrated with teams, business processes and existing products.
For teams
Save time on searching, writing, checking, documenting or producing.
For business processes
Connect AI to documents, operations and existing tools.
For products and customer experience
Embed AI into products and services to improve the experience, personalise journeys and create new uses.
03 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.
04 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.
05 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.
06 Our experts
AI takes several kinds of expertise. The firm brings them together.
An AI use case in production never rests on a single skill: you need to understand the business, prepare the data, build the infrastructure, secure it and drive adoption. The firm makes these expertises available, mobilised as your project needs them. The Forward Deployed AI Engineer carries delivery end to end, surrounded by the specialists each project calls for.
- 01
Forward Deployed AI Engineer
An engineer embedded in your teams: they carry a use case end to end, from business diagnosis to adopted production, and stay accountable for the result.
- 02
AI Engineer
Designing and writing the code that runs the use case: agents, prompts, tools, orchestration, integrations, tests and shipping.
- 03
Data Engineer
Preparing, qualifying and connecting your data to the agents: data foundations, RAG, evaluation and answer quality.
- 04
Platform Engineer
The infrastructure that runs AI: cloud, MLOps and LLMOps, observability, reliability and cost control (FinOps).
- 05
Security & Governance Engineer
Access management, traceability, sensitive data, compliance and human oversight, built in from the design stage.
- 06
IT Integration Engineer
Connecting AI to your existing tools, workflows and systems: ERP, CRM, DMS and business applications.
- 07
Training & Adoption Lead
Levelling up teams, acculturation and internal champions, so usage takes hold over time.
-
The right expertise, mobilised at the right moment, to take your use cases all the way to production.
07 First references
AI services already in production.
Two first references, genuinely in use, on a foundation that's reusable from one case to the next. Technical detail remains available to your technical teams.
Energy
Triaging and resolving recurring network incidents in the NOC.
An AI-agent platform adopted by the network operations centre (NOC) teams: it triages the most frequent alerts and now autonomously resolves around half of recurring monitoring incidents, with a human in the loop on sensitive cases.
Why it matters: teams don't just save time on triage, they see recurring incidents get resolved, without ever losing control. Autonomy stays confined to deterministic, reversible cases validated by the team.
Read the case study
WealthTech · Wealth-management software
Industrialising agentic AI across engineering.
A shared agentic-AI practice, deployed across more than 20 engineering teams, from AI-assisted coding to vulnerability remediation, with common standards.
Why it matters: AI doesn't stay confined to a pilot team; it becomes a shared, governed and repeatable practice.
Read the case study
The founder
« AI only creates value the day it truly runs, adopted by the teams. Our role: close the gap between prototype and production, and own the result end to end. »
A cloud and GenAI engineer, he designs and deploys AI platforms in production on AWS. He has led AI agents and data foundations in demanding environments (energy, finance, software), from data through to adopted usage. He founded Intelligence Partners with one simple conviction: turning the right AI use cases into services that actually run in production.
Dorian Richard
Founder · Intelligence Partners
Technologies that make the difference
Claude 
Mistral AI 
Meta AI 
OpenAI 
Gemini 
OpenCV 
Python 
PyTorch 
Keras 
TensorFlow 
Apache Airflow 
AWS 09 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.
10 Blog
The blog
Our take on scaling AI in the enterprise: use cases, integration, security and adoption.
6 min
AI Platform Engineering: exploration, capitalisation, industrialisation
A free playground, shared capitalisation, strict industrialisation: the AI Platform Engineering method for taking AI agents from prototype to production.
Read the article 7 min
MCP architecture in a regulated environment
Exposing many MCP servers to an agent without widening the attack surface: read-only by default, strict domain separation, tightly scoped permissions.
Read the articleLet's talk
Want to identify the right AI use cases for your company?
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