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Arixent

AI Engineering

AI Consulting, Strategy & Governance

Teams arrive with more AI ideas than they can fund and no agreed way to compare them. We assess data readiness, business value and risk, then turn that list into a sequenced roadmap with clear ownership and review controls.

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Overview

AI strategy is a list of decisions, not a slide deck. Which problems are worth solving with AI, which data can support them, what it will cost to run, and what has to be true in your organization for any of it to stick. Our AI consulting services produce those decisions with the people who will have to live with them.

We come at strategy as engineers. Every recommendation is grounded in what we have seen work in production, and every roadmap item comes with an honest view of feasibility, dependency and risk. The output is a plan your engineering leaders can start executing the following week.

This page also covers responsible AI and governance, because a plan that cannot be defended to a regulator or an auditor is not a finished plan.

Formalizing this capability across the company? See our AI Center of Excellence service.

This service is part of AI Engineering, our broader practice for this work.

Offering 1

AI strategy and readiness

AI strategy is a set of decisions, not a slide deck. We help you decide which problems are worth solving with AI, whether your data can support them, and what has to be true in your organization for any of it to stick.

Use-case discovery workshops

Structured sessions with business and technology teams to surface candidate use cases and score them on value, feasibility and data readiness.

AI readiness assessment

A frank review of data, platforms, skills, security posture and operating model against what the target use cases require.

Feasibility studies and proofs of value

Short, time-boxed experiments on your data that answer whether a use case will work before you fund it.

AI roadmap and business case

A sequenced plan with investment estimates, expected outcomes, owners and the foundations each phase depends on.

Typical use

  • Executive teams that need a credible AI plan for the next budget cycle
  • Global capability centers defining their AI charter and first delivery portfolio
  • Enterprises that ran pilots which never reached production and want to understand why

Offering 2

Responsible AI and governance

Responsible AI is what lets you ship AI to customers, regulators and auditors with confidence. We help organizations put governance into practice with policies engineers can follow and controls built into pipelines.

Security analyst studying data on dark monitors

AI risk assessment and classification

Inventory of AI systems, risk tiering and the controls each tier requires.

Bias and fairness testing

Measurement across protected groups, mitigation strategies and ongoing monitoring.

Privacy and data protection

Data minimization, consent handling, anonymization and residency controls in AI pipelines.

Security red-teaming

Prompt injection, data exfiltration, jailbreak and abuse testing for LLM applications and agents.

Typical use

  • Banks and insurers deploying models that affect credit, pricing or claims decisions
  • Companies preparing for EU AI Act obligations on high-risk systems
  • Product companies that want a security assessment of their LLM features before launch

Want this for your product? Talk to an engineer who has built it.

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How we work

How we deliver

  1. 01

    Align

    Interviews and workshops to understand goals, constraints and what success would look like in twelve months.

  2. 02

    Assess

    Data, platform, skills and governance review against the shortlisted use cases.

  3. 03

    Prove

    One or two proofs of value on real data to de-risk the highest-value candidates.

  4. 04

    Plan

    Roadmap, business case, operating model and a first delivery increment ready to start.

Stack

Tools we work with

We are neutral on tooling and pick what fits your environment, your team and the cost you can sustain.

  • Use-case scoring frameworks
  • Data readiness audits
  • Cost modeling
  • AWS, Azure and GCP AI service evaluation
  • LLM provider assessment
  • Fairlearn
  • SHAP
  • EU AI Act
  • NIST AI RMF
  • India DPDP Act

Bring the problem. We bring the team.

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Industries

Where we apply this most

Relevant domain requirements include Banking, Financial Services & Insurance and Healthcare & Life Sciences.

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Banking, Financial Services & InsuranceDocument intelligence, risk models, fraud detection and modern digital banking experiences.Explore
Clinician in scrubs using a tablet in a hospital corridor
Healthcare & Life SciencesClinical documentation, patient engagement, imaging AI and compliant data platforms.Explore
Row of white robotic arms on an automated production line
Manufacturing & IndustrialPredictive maintenance, quality inspection, digital twins and plant analytics.Explore
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HR Tech & StaffingCandidate matching, recruiter copilots, workforce analytics and ATS/HRMS platforms.Explore

FAQ

Questions we hear often

Typically a few weeks, depending on how many business units are involved and whether proofs of value are included. We agree the scope and timeline before starting.

No. When a commercial product or a platform feature solves the problem well, we say so. Our value is in the decision being right, not in creating downstream work.

A prioritized use-case portfolio, readiness findings with remediation, a sequenced roadmap with estimates, and a governance and operating model proposal. Where proofs of value ran, you also get working code and results.

It speeds it up after the first project, because teams stop rediscovering the same questions. Controls built into pipelines run automatically; reviews are scoped to risk, so low-risk features move fast.

Related services

You may also need

Generative AI & LLM Applications: LLM applications, RAG, conversational AI and AI features inside the products you already run.

AI Agents & Intelligent Automation: Agentic systems and automation that complete multi-step work under human oversight.

Data Strategy, Architecture & Governance: Target architecture, operating model and the governance that makes data trusted and AI-ready.

Generative AI & LLM Applications

LLM applications, RAG, conversational AI and AI features inside the products you already run.

Learn more

AI Agents & Intelligent Automation

Agentic systems and automation that complete multi-step work under human oversight.

Learn more

Data Strategy, Architecture & Governance

Target architecture, operating model and the governance that makes data trusted and AI-ready.

Learn more

Machine Learning, Vision & MLOps

Forecasting, vision and edge models with the MLOps that keeps them reliable after launch.

Learn more

Next step

Ready when you are.

Tell us what you are trying to build and we will come back with a point of view, not a pitch.

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