Engagement Lead
Owns the relationship, the delivery plan, and the flow of communication between your team and the pod. Your primary point of contact throughout.
One way of working, whatever the scope.
Every Autonomix engagement follows the same structured approach, whether it is a six-week MVP or a multi-function enterprise rollout. Here is how the teams are built, how delivery runs, and how engagements are priced.
Your delivery pod 6 specialists
Managed by Autonomix, accountable to you
How we deliver.
Five phases, each with a defined output. Nothing moves forward until the output of the last one is in place, which means no surprises mid-build and no handoff that leaves you with something you cannot use.
- 01
Discovery & Planning
A prioritised opportunity map and agreed success metrics.
It starts by mapping your goals, systems, and data sources, and pinpointing the workflows where AI creates the most value. Success metrics are agreed before any build begins.
- 02
Design & Architecture
A documented agent architecture, security model, and integration plan.
Agent workflows, the security model, and governance are designed up front. Orchestration, access controls, and integration points are all defined before development begins.
- 03
Development & Testing
Tested agents running in your actual environment.
Agents are built and integrated in your real environment, not a sandbox, and tested against real data and real workflows before anything moves forward.
- 04
Deployment
Agents live across the agreed functions, with your team trained alongside them.
Agents roll out across the relevant products and functions, with training and change management included. Deployment is a milestone, not the finish line.
- 05
Data-Driven Optimisation
Ongoing performance reports and a continuous improvement cycle.
Performance is monitored, feedback collected, and agents retrained where needed. The goal is sustained value over time, not a launch followed by a handoff.
How we show up for you.
Every engagement is staffed by a dedicated pod, composed for your work from six specialist roles and managed entirely by Autonomix. You work with the pod. You do not manage it.
AI / LLM Engineer
Designs and builds the agent logic, prompt architecture, and model integration. Responsible for how the agents think and respond.
Integration Engineer
Connects agents to your existing systems: CRMs, ERPs, databases, and communication tools. Nothing is left sitting in isolation.
Data & Analytics Engineer
Builds the pipelines, retrieval infrastructure, and dashboards the agents depend on. Owns data quality and performance measurement.
UX / Workflow Designer
Designs the interfaces and workflows around the agents, so the system fits how your team actually works day to day.
QA & Evaluation Specialist
Tests every agent against real scenarios before and after deployment. Owns accuracy, reliability, and catching failure modes before your users do.
Embedded, not external
The pod works inside your environment, your tools, and your sprint cadences. No managing from a distance and reporting back once a week.
Phase-gated with working demos
Every major phase ends with something you can see and test. A working output you can evaluate, not a status report or a slide deck.
Managed by Autonomix, accountable to you
You set the direction and the requirements. Resource management, technical decisions, and day-to-day delivery are handled for you, end to end.
Built to scale with you.
Engagements start at the right size and expand as scope grows. The same pod model applies at every stage. The team composition changes; the approach does not.
Focused Engagement
Best for. A single use case, one business function, an MVP or proof of concept.
A small, tightly scoped pod and fast iteration, with a working output in weeks rather than months. A concrete first step before committing to a larger programme.
Typical clients. Growing businesses starting their first AI engagement, or larger organisations validating a use case before they scale it.
Growth Programme
Best for. Two to four parallel workstreams across one or two functions.
A larger pod with expanded skill coverage, running multiple workstreams in coordinated sprints, with regular steering sessions with your leadership.
Typical clients. SMBs scaling a validated use case, or enterprise teams rolling out across a department.
Enterprise Scale
Best for. Multi-function, multi-product AI rollout across a large organisation.
Multiple coordinated pods across functions, with governance frameworks, change management, and organisation-wide training. Reporting tied to enterprise KPIs.
Typical clients. Enterprises running AI as a strategic programme across sales, finance, legal, HR, and operations at once.
How we structure the commercial engagement.
Two models are available now, and one is available on selected engagements. Each fits a different scope and working relationship. Not sure which fits? Talk it through with us on an initial call.
Fixed-Cost Project
Best for defined scope and deliverables.
Clear milestones and pricing agreed upfront. You know what you are getting and what it costs before work starts. Well suited to MVPs, integrations, and targeted deployments.
Dedicated Team / Pod
Best for ongoing or large-scale programmes.
A full-time or shared Autonomix pod embedded into your business, managed by Autonomix with flexible scaling as scope evolves. Best where continuity matters.
Outcome-Based
Exploring next-generation engagement.
Pricing tied to measurable outcomes or platform access rather than time and effort. Available on selected engagements where the scope and measurement framework are a fit.
Not sure which fits your situation? Start with a conversation. We will tell you honestly which approach makes sense for your scope.
Book a callWhat our clients say.
This is fantastic. The Award Wizard going live with the Enhance and Review feature is going to be a game-changer. It will not only save a lot of time but also take the pressure off when it comes to drafting strong citations.
Autonomix has been fantastic in keeping momentum across sprints. With core modules like benefits enrollment, eligibility logic, and admin dashboards coming together, we are already tracking ahead of schedule.
I really appreciate how well our collaboration has been going and look forward to bringing our shared vision to reality together, building something that genuinely changes how ventures are built and scaled.
My thanks to the Autonomix team for a great meeting. The Roller POS integration into Samsung VXT worked like a charm. The team and I learned a lot and are excited about the forward momentum.
We built PitchLynk because we had seen too many great ideas die in the fundraising gap. AI changes that equation. Turning that into a product people actually trust, one that lives on their phone, handles sensitive deal flow, and genuinely changes how fundraising works, that is where execution has to match ambition. With Autonomix, it did.
Ready to talk through your use case?
Tell us what you are trying to solve. We will tell you whether AI is the right tool, what a realistic engagement looks like, and what it would cost.