AI Architect
Converts the business objective into a technical approach. The AI Architect defines system design, data flow, integrations, and guardrails, leading to a defined outcome.
AI Squad
Your own AI team,
for one business goal
You may know how AI could help your business. The harder part is deciding what to build, how to measure its value and who takes it to a working prototype. An AI Squad brings business and engineering together to deliver it.
Why it matters
Almost everyone uses AI.
Few make it count.
88%
of respondents use AI in at least one part of their business, yet the majority are still at the experimentation or pilot stage.
McKinseyState of AI, 2025The difficulty is no longer adopting AI. It’s knowing how and where it can make a concrete impact.
An AI Squad answers both: one team, one defined opportunity, and the KPIs to show what it’s worth.
What is an AI Squad?
Good AI ideas stall.
A squad keeps them moving.
Not a one-off AI project. The squad aligns your commercial goals with technical delivery.
The AI SquadA small team with a defined purpose, market-fit criteria and system KPIs.An AI Squad brings your organisation’s talent and our engineering together on one AI project, builds the system and reviews the outcomes once it’s live.Meet the team
When AI projects lose momentum
A good idea can still stall: tools applied without priority, experiments on unvalidated assumptions, and no engineering to take a successful test to production. Time goes into projects with no KPIs or measurable ROI.
Focused and measurable
The squad focuses on one defined AI opportunity. Market-fit criteria, system KPIs and ROI goals are set up front, then reviewed once the system is live.
A real path to production
As the work grows into a functional business system, senior engineers stay responsible for its architecture, quality and scalability, so a successful experiment becomes a production system that’s ready to scale.
Meet your AI Squad
Architecture, engineering
and design in one team
A single focused team covers architecture, engineering, and design. A system must be self-explanatory in its function. Those responsibilities are all embraced by your AI Squad.
AI Native Engineers
Develop, validate, and deploy the solution, applying AI at all engineering stages. They integrate it with your current systems and stress-test it in real-world situations.
Designer
The Designer clarifies workflows, interfaces, and human review points, so that your team understands and can utilize what they've created.
Why act now
Standing still is a costly choice
AI is now a baseline, not an experiment.
Arithmiks gets you to production first.
R&D capitalCapital burns on unguided experiments
CompetitionRivals ship production AI faster
SecurityUnmanaged AI adds risk and tech debt
The advantageProduction-ready in weeks, not months
We align your business goals to a leading industry solution, with security built in and a production-ready system delivered by senior engineers.
The Arithmiks AI Squad
From objective to handoff
Core focus
Market fitSystem KPIsWorkload vs. business goalsSuccess criteria
What you receive
- AI Squad formationThe right people, organised around your goal.
- ROI metricsHow the system’s value will be measured.
- TimelineWhen each part of the work lands.
STAGE 01
Prioritise Market Fit
A team built around your goals
Duration1–2 weeks
Core focus
Solution modellingAI-native developmentSenior-led architectureQuality and scale
What you receive
- Functional AI systemBuilt for real users, not a demo.
- ScalabilityArchitecture that grows with demand.
- MonitoringVisibility into how the system performs.
STAGE 02
Build a Production-Ready System
Model and build the solution with AI-native skills.
Duration3–6 weeks
Core focus
Engineering documentationKPI reviewExpectations vs. delivery
What you receive
- Expectation-to-results analysisSystem KPIs and goals compared with what was delivered.
- System handoff documentationEverything your engineers need to own it.
STAGE 03
Hand Off
Compare expectations with delivery, then hand over ownership.
Duration1–2 weeks
Outcomes & business impact
What changes
for your business
Focus
AI investment focused on business goals
Progress
Measurable progress against KPIs and ROI
Delivery
Production-ready systems for real users
Ownership
Senior engineers own quality and scale
Handoff
A clear handoff with docs and KPI analysis
Ready to move an AI opportunity from planning to production?
Book a ConsultationReal-world cases
Proven by industry leaders.
Built for your team.
These examples show how top companies have used AI in their workflows, and how an Arithmiks AI Squad can build similar solutions for your team.
Book a ConsultationInsurance
Insurance claims processing
Simple claims stall during catastrophe spikes. Allianz built AI agents that check coverage, weather, fraud and payouts, with human approval.
Arithmiks blueprint
- Approval gates on policy data
- A dashboard showing the reasoning behind every payout
Financial services
Internal knowledge assistant
Morgan Stanley backed its AI assistant with specific evaluation matrices and daily quality checks.
Arithmiks blueprint
- Permissioned data pipelines
- Automated tests that catch errors
- Clean interfaces with precise sources
Retail
Demand forecasting
Legacy seasonal models couldn’t keep up with demand shifts. Walmart piloted machine-learning forecasting in some departments, then expanded it company-wide.
Arithmiks blueprint
- Focus and scope first
- Models backtested on historical sales
- Scalable data pipelines
- An interactive review view for planners
Legal
Legal research & drafting
Legal teams need AI output they can verify. CoCounsel draws on trusted legal databases and links source citations inside document editors.
Arithmiks blueprint
- Approved sources and access controls
- Drafting inside familiar apps like MS Word
- Every claim linked back to its source
Automotive
Inbound call capture
Showroom teams were missing incoming call leads. Autotorino’s AI captures calls, classifies intent and fills in CRM lead records.
Arithmiks blueprint
- Call-handling rules mapped
- Speech-to-text connected to your CRM
- Instant lead alerts for your sales team
FAQs
Questions we hear
before week 1
Something we haven't covered? Ask us on the call.
When your organisation is ready to commit to an AI initiative but needs support organising the work, defining measurable outcomes, developing the system or getting it ready for production.
Yes. The engagement includes production-level architecture and development, monitoring, KPI analysis and engineering handoff.
By measurable ROI. The team works to your business objectives and system KPIs, and at handoff, expectations are compared with what was delivered and how the system performs.
An AI Squad engagement takes 5 to 10 weeks: 1–2 weeks to prioritise, 3–6 weeks to build, and 1–2 weeks for handoff and training.
Your team does. You get the full source code repository, technical documentation, and spend and token-usage tracking, so your internal team can maintain it.
Describe your idea
Tell us what you're building. We'll read it before the call, so the 30 minutes go on your problem rather than introductions.
Omer and his team put in significant effort and delivered many positive contributions.
Louis-AntoineCTO at GO, Founder at Web2 Web3
