From zero to one
We identify the priority process, redesign the workflow, and build the first AI system around a clear business metric.
30-minute initial diagnostic
We implement AI to improve your business processes.
We help companies redesign and improve processes with AI so they operate faster, at lower cost, and with better quality.
Whether you are starting from zero or have a stalled pilot, we turn a business problem into a solution that works in real operations.
We design the process, build the system, and choose the architecture around your priority: more control, independence, privacy, and security, or more speed and lower cost to reach production.
Zero to one or stalled-pilot recoveryPrivate or local infrastructure when usefulMulti-model architecture for speed and cost
End-to-end implementation
From the first use case to production, without vendor lock-in.
We work with technologies from Google Cloud, AWS, NVIDIA, Intel, Meta, and LangGraph, among others. We choose each component around the process, the data, the required level of control, and the speed the business needs.
Recovery for difficult projects
We audit failed or stalled pilots, find the block in data, integration, adoption, or governance, and move them into controlled operation.
Infrastructure that fits the priority
We design private or local infrastructure for maximum control, or multi-model systems for faster and lower-cost production.
From technology to process
Businesses do not buy AI. They buy resolved processes.
A chatbot answers questions. An agent connects to your systems, proposes an action, and executes it when authorized. That is what AI looks like when it leaves the lab and enters the operation.
Finance & back office
Reconciliation agent
Matches bank statements with accounting systems, suggests automatic matches, and escalates only the exceptions that require human judgment.Operations & logistics
Supply chain agent
Anticipates stockouts and proposes replenishment orders to planners while learning from each validated decision.Sales & proposals
Quoting agent
Reads RFPs and emails, retrieves catalog information, and prepares proposal drafts for commercial review.Human resources
High-volume hiring agent
Screens applications, summarizes profiles, and prioritizes candidates using criteria defined by the talent team.IT & internal support
Level 1 agent
Handles frequent requests, documents tickets, and routes complex cases with complete context.Legal & compliance
KYC audit agent
Reviews corporate documentation, detects inconsistencies, and prepares files for legal review.See applied use cases
Reference cases based on documented implementations in banking, retail, telecom, and professional services. Specific results vary by case.
Anonymous case studies
Real projects and measurable outcomes without exposing clients.
Because of confidentiality, we do not name companies or publish figures without an approved baseline. We do show the scope, evaluation window, and operational metric used for each case.
Customer support, orders, and reminders agent
The conversational layer was connected to commercial rules and operating workflows so AI could move real tasks instead of only answering informational questions.
Actual solution
More consistent support, better order capture, and automated follow-up.Customer success AI agent that answers support questions, takes orders, sends reminders, and escalates exceptions to the commercial team.
- Scope
- Customer questions, order intake, reminders, and escalation to the commercial team.
- Evaluation window
- Controlled pilot and the first weeks of operation.
- Operational metric
- Share of conversations ending in a captured order or an escalation with complete context.
IT support ticketing agent
An assisted flow classified requests, asked for missing information, documented the issue, and routed only the cases that truly required human intervention.
Actual solution
Tickets created with better context and frequent requests resolved automatically.Ticketing system with an AI agent that turns user-reported problems into structured tickets and automatically resolves repetitive cases when it has enough context.
- Scope
- Inbound support requests and recurring level-1 cases across a multi-site operation.
- Evaluation window
- Controlled pilot and the first weeks of operation.
- Operational metric
- Percentage of tickets with complete context and automatic-resolution rate for recurring requests.
Automated pallet planning
Operational data and business rules were connected to generate actionable plans, reduce friction between areas, and accelerate distribution decisions.
Actual solution
Faster planning, less manual adjustment, and better operational coordination.Automation for pallet planning using dispatch constraints, volume, priorities, and the lab's operating rules.
- Scope
- Pallet planning across dispatch constraints, volume, priorities, and the laboratory's operating rules.
- Evaluation window
- Per-cycle comparison between the baseline and the controlled pilot.
- Operational metric
- Planning time per cycle and number of manual adjustments before dispatch.
Stop improvising. Start operating.
The difference between an abandoned pilot and a value-generating agent is the architecture, governance, and adoption model behind it.
Traditional approach
- Isolated pilots stuck in lab environments.
- Unpredictable API costs without budget control.
- Generic models without security or business context.
IMR framework
- Structured path to cloud, local, or hybrid infrastructure.
- Budget, usage, and human-authorization guardrails.
- Security, traceability, and compliance by design.
AI doesn't fail from lack of capability. It fails when introduced into systems not designed to absorb it.
In regulated environments, AI adoption isn't a computing power problem, but one of systemic friction. Most failures occur in the last mile: integration.
Structural integrity is not an afterthought.We don't build black boxes. We design architecture that allows AI to operate with transparency, auditability, and effective human oversight. We understand that technology must serve human judgment, not blindly replace it.
We build resilient systems capable of adapting to changing regulations without sacrificing innovation speed.
How we work
A short, controlled, measurable path to production.
We do not start by installing tools. We start by choosing a process where AI can create value and where the business can measure it.
30 min + initial review
1. Diagnostic
Deliverable: Opportunity map and effort/impact prioritization.
What we need from the client: Access to process leaders and context around the current pain.
1 to 2 weeks
2. Use-case design
Deliverable: Target workflow, human controls, architecture, and success metric.
What we need from the client: Data examples, business rules, and operational constraints.
3 to 6 weeks
3. Controlled pilot
Deliverable: Working agent with pilot users, logs, and acceptance criteria.
What we need from the client: Reference users, weekly feedback, and secure access to systems.
6 to 12 weeks
4. Production and improvement
Deliverable: Monitored operation, impact dashboard, and improvement backlog.
What we need from the client: Internal owner, change governance, and business metrics.
Our services
Three ways to implement AI, based on where you are starting.
Whether you are starting from zero, have a failed pilot, or need more control over your data, we design the process, system, and infrastructure required for measurable operation.
From zero to one
We select a high-impact process, establish its baseline, redesign the workflow, and build the first AI use case with real users and a success metric.
- Prioritized opportunity map
- Target process and use case
- First measurable system in operation
Applied AI by industry and location
Explore specific pages with scope, deliverables, evidence, and frequently asked questions for each context.
Who builds your AI matters once it reaches production.
The difference between an abandoned pilot and a value-generating agent is the team that creates, operates, and improves it.
We arrive to execute, not just to sell.
We design agents that work, not pretty decks. Every initiative starts from a real process and a business metric.
We look for subsidies and incentives when they apply.
We evaluate support programs from providers or technology ecosystems to reduce friction without locking the architecture into one path.
The senior team stays after the sale.
We stay involved in implementation, enablement, measurement, and continuous improvement.
Local response, global perspective.
We combine operational proximity with modern architecture, security, and delivery standards.
Why working with IMR Tech changes the outcome.
The advantage is not only technical. It is having one team connect adoption, architecture, operations, and metrics into a single path to production.
| Internal team | Generalist implementation | IMR Tech | |
|---|---|---|---|
| From diagnostic to production | Depends on internal bandwidth | Often ends in recommendations | Design, build, deployment and operation |
| Real adoption by area | Fragmented by team | Generic enablement | Role-based cases, champions and usage measurement |
| Architecture and security | Specific experience may be missing | Varies by provider | Human controls, traceability and governance by design |
| Costs and FinOps | Hard to monitor early | Often out of scope | Dashboards, alerts and consumption policies |
| Operational continuity | Competes with internal priorities | Partial handoff | Runbooks, follow-up and monthly improvement |
| Open, cloud, or local stack | Learning curve | Not always specialized | Technical fluency to choose provider, model, and infrastructure |
A senior team to bring AI down to earth.
IMR Tech is led by David Clerc and a team focused on execution: business strategy, cloud architecture, automation, and agent implementation. We work with business leaders and technical teams to identify processes, design useful agents, and move them into operation without losing control over data, costs, or decisions.
AI only matters when it changes how the business operates.
The first step is choosing the right process.
Tell us what process you want to improve, whether you are starting from zero, or where your project is stuck. We will save the request and show the next steps to schedule or continue on WhatsApp.
Frequently asked questions about agentic AI for companies
Questions you are probably asking
It is an AI implementation pattern that does more than answer. It reasons about a goal, consults systems, proposes actions, and executes steps with authorization and traceability.
They usually fail because they are not integrated with real systems, lack governance, have weak metrics, create cost uncertainty, or are not adopted by operating teams.
It depends on the process and integrations, but a controlled first implementation can often be designed, tested, and measured in 6 to 12 week cycles.
We measure time saved, error reduction, adoption by role, operating costs, processed volume, and the quality of decisions escalated to humans.
Start with a usage baseline, high-impact repetitive processes, and an adoption route by area. It may be Gemini, Copilot, ChatGPT Enterprise, or another tool; the point is turning licenses into operation.
We use cloud, identity, and access-control practices, action traceability, human review, and principles from frameworks such as NIST AI RMF, OWASP for LLMs, and ISO/IEC 42001 when relevant to the use case.
Your legal team interprets the regulation. We produce the technical evidence: architecture, traceability, data residency, access controls, and decision logs to support cybersecurity and data-protection review.
We can start from what exists. We inventory agents, assess observability, risks, costs, and dependencies, then propose an operating or improvement plan without rewriting what already works.
We work in phases with clear decision points. Deliverables, intellectual property, exit terms, and responsibilities are defined before starting, without fine print.
Not for the diagnostic. For implementation, we define environments, permissions, and data samples according to the use case. The rule is minimum necessary access and explicit controls before connecting production systems.