Afinico AI
Reimagine how your organization works with AI
For decades, transformation meant putting new technology over the top of the way people already worked. That approach doesn't hold with AI. Leaders now need to rethink how work gets done: where AI takes on the work, and where it amplifies human intelligence, judgment and decisions. That's how organizations improve performance and grow enterprise value with AI.
AI needs organizational context to change how an organization performs
A language model on its own doesn't transform anything. It knows a lot about the world and nothing about your clients, your teams or how decisions get made in your business. Getting real value takes a deliberate effort to build organizational context for the decisions that matter, from the data and knowledge that already exist inside the organization.
How most organizations use AI today
Bottom up
They start by automating tasks, and are now starting to automate workflows and hand them to AI agents. Each step saves time, but the organization still works the way it did before.
How Afinico approaches it
Top down
We start with how the whole organization works together, and the decisions that drive performance and value. Then we build AI that improves them.
That's why we built Afinico Neura.
It shows the dynamics across complex team, client, partner and organizational relationships and workflows. Afinico AI uses that context to build AI systems for your organization: automating work and building agentic workflows, helping teams work better together across those workflows, and delivering the performance and enterprise value the business needs.
How we do it
From the decisions that matter to AI that runs the work
Start with the decisions that matter
Find where performance and enterprise value are won or lost, before choosing any tasks to automate.Start with one, then build out
Pick one process, workflow or client relationship. Prove the value there, then extend to the next.Build the context for those decisions
Bring together what your systems hold, what your people know, and how your teams really work together, using Afinico Neura.Redesign the work
Decide what AI does, what people do, and where a person must approve. Then automate the work and build the agentic workflows around it.Bring the teams with it
Use Afinico Neura to see whether teams are working together across the new workflows, and where adoption is stalling.Run it inside your environment
On models you choose, under your own governance, with your data staying where it is.
What sits underneath
The same building blocks in every system we build
Every step above rests on the same parts: your communications and records, Afinico Neura holding the context, the models you choose, and the AI systems your people use, with one set of rules over all of it. Select any part to see what it does.
Afinico Neura
Holds that organizational context and keeps it current, so every AI system built on top knows how your organization works.
Our track record
Workflow automation with AI
We built an AI system that automates a complex deterministic workflow, across multiple geographies, clients and teams, to amplify the work that humans do across the workflow.
Other transformations
We have led complex operating model transformations, turnarounds and AI‑led change in global technology services, healthcare and government organizations.
10× ARR
growth of a global tech services business
600%
operating earnings uplift in year one
Large M&A
deals in the US, UK and Asia Pacific
Loss to profit
sustained, through turnaround
Global enterprise operating model transformation
Reshaping how a multi-billion dollar enterprise operates and performs, through AI-enabled innovation, operating model redesign, stronger governance and better coordination across global teams.
Global technology services transformation
Turning a global technology services business into a scalable, recurring-revenue platform, delivering 10× revenue growth to $500M+ annual recurring revenues.
Technology platform turnaround
Strengthening leadership, governance and execution across a $250M+ technology business, with 150% improvement in EBITDA and a return to profitability.
Marketing technology and AI transformation
Changing how a marketing technology business operates through innovation, operating model redesign and faster execution, increasing operating earnings by 600% in one year and restoring profitability.
Healthcare operating model transformation
Modernizing healthcare operating models, digital services and investment governance, for smarter capital decisions, better operational performance and service delivery that scales.
Government services transformation
Redesigning government operating models, governance and service delivery, improving coordination, operational effectiveness and performance in complex, secure environments.
What else drives productivity, performance and value
- Measure in value, not activity. Judge AI by its effect on revenue, profit and enterprise value, not by the number of tasks automated.
- Treat context as an asset. Your organizational context is what makes AI useful in your business. Build it on purpose and keep it current.
- Redesign roles alongside the work. When AI takes on part of a job, decide what people will do with the time and judgment it frees up.
- Own your models and your data. Depending on one provider sets your costs, your roadmap and where your data goes.
- Govern from day one. Decide early what AI can see, do and decide, and bring your people and their representatives with you.
Sovereign AI
Sovereign AI for team productivity
Most teams ration their AI against a provider's budget and limits, and send their data outside the organization to use it. A sovereign AI system runs open models inside your own environment, each chosen for the task. Your teams use AI as much as the work needs, and you decide where your data and code go.
A different approach
Smaller open models, each matched to a specific task and run where you choose. For many jobs, a focused model does the work just as well, at a fraction of the cost.First use: engineering productivity
Helping software teams write, review and test code faster, with the models running under your own governance.We use it ourselves
Afinico's own engineers used these models to help build a live enterprise AI system.What you get
Significantly lower cost, less dependence on any single provider, and control over where your data and code go.
Indicative scenario
What it means for 300 engineers
An indicative scenario for a software engineering team of 300, using typical market prices and cautious assumptions. Your figures may differ.
Assumed: large provider subscriptions
- About $500 per engineer per month, subscription and usage included
- $150,000 a month, $1.8 million a year
- Usage capped at about 25 million tokens per engineer per month
Your own model
- About $1 million over three years to buy, set up, host and maintain
- Handles around 60% of the work; provider subscriptions kept for the rest
- No usage cap on the work it handles
- to pay back, depending on usage
- 6 to 11 months
- net saving over three years
- $2.2 million or more
- on usage for the work it handles
- No cap
The more your engineers use AI, the sooner it pays back
- At the assumed budget: about 11 months. Engineers stay at about $500 a month each and around 60% of the work moves to your own model.
- As usage grows: about 6 months. When a fifth of engineers use four times as much, spend rises to about $240,000 a month. Moving around 70% of the work to your own model saves about $168,000 a month.
The bigger gain is productivity
As engineers get more productive with AI, provider costs rise with them. With your own model, usage can grow without the bill growing, so the case gets stronger the more your engineers use it.
Indicative only. Based on an assumed $500 per engineer per month, rising where some engineers use more, 60% to 70% of work moved to your own model, and $1 million total cost over three years including equipment, setup and maintenance.