Product strategy
Where a model creates value the customer will pay for, and what is better solved without one.
At Infynix, we craft growth engines that inspire and endure.
Building smart software tools powered by Large Language Models and AI agents. Connecting AI safely to internal files without data leaks or bad answers is complex. AI should solve real business tasks by connecting directly to your company data.
OUR CAPABILITIES IN
AI Product Development & Agents
01
RAG Integration
02
Autonomous AI Agents
03
Prompt Guardrails
04
Model Tuning
How We Work
01
Use Cases
We find high-impact tasks for AI automation.
02
Models
We select OpenAI, Claude, or open-source models.
03
Data Pipelines
We format your company files into vector databases.
04
Agent Logic
We program agent workflows using Python.
05
Testing
We enforce guardrails to keep answers accurate.
06
Deploy
We launch tools to internal teams with usage logging.
AI product development for UK startups and established companies building software where a model is part of the product: assistants that work inside a customer’s workflow, agents that complete tasks across systems, document and data products, vertical tools for law, finance, property, healthcare, recruitment and construction. We build them from our office in Dagenham with the engineering discipline that separates a demo from a product a customer will pay for and a regulator will accept.
Selling AI products in the UK and into Europe means answering questions early. Where is the data processed and by which models? What is the lawful basis and where is the DPIA? How is accuracy measured and what happens when the model is wrong? Which EU AI Act category does the product fall into if it is sold across the Channel? What are the costs at scale? Founders who have those answers close enterprise deals; those who do not stall at procurement.
So we build the product and the evidence together: evaluation sets that measure quality on real tasks, guardrails and human-in-the-loop where the risk requires, cost controls per customer, private or UK-region deployment where the data demands, observability of every model call, and the documentation a buyer’s security and legal teams will ask for. The model is treated as a component that will be replaced, so the product survives the next release from any vendor.
Founders raising or recently funded, established software companies adding AI to their product, professional services firms productising expertise, and businesses building an internal AI capability that must meet enterprise standards.
What we build
Assistants and copilots
Conversational and embedded assistants working inside the customer’s workflow with their data.
Agents
Systems that plan and complete tasks across tools and systems, with permissions and review points.
Retrieval and knowledge
Pipelines that ground answers in customer documents and data with sources shown.
Evaluation
Test sets, quality scoring, regression testing and release gates so quality is measured, not felt.
Guardrails and governance
Input and output controls, human review, logging and the documentation buyers ask for.
Platform engineering
Multi-tenancy, billing, cost control per customer, observability and UK or private deployment.
Model strategy
Commercial, open-weight and small models selected per task and swappable as the market moves.
Engagement stages
| Stage | Typical duration | Output |
|---|---|---|
| Product discovery | 2 to 3 weeks | Use cases, risk assessment, architecture, evaluation plan |
| Prototype | 3 to 6 weeks | Working prototype with measured quality on real tasks |
| Product build | 8 to 20 weeks | Production system with evaluation, guardrails, billing, observability |
| Enterprise readiness | 2 to 6 weeks | Security, privacy and AI governance documentation, private deployment options |
| Launch and iteration | Monthly | Releases driven by usage and evaluation data |
| Embedded team | Monthly | Engineers working inside the product team |
Who it is for
Funded startups
A first product built to be measured, sold and scaled.
Software companies
AI features that fit the existing product and its customers’ compliance needs.
Legal and financial tech
Products where accuracy, audit and privacy are the product.
Property and construction tech
Document-heavy workflows turned into products.
Health tech
Products built with the governance the sector requires from the start.
Internal platforms
Enterprise AI capabilities built to the same standard as a product.
Ways of working
Discovery and prototype
Fixed-scope work that proves value on real tasks before a larger commitment.
Product build
A dedicated team taking the product from prototype to paying customers.
Embedded engineers
AI engineers working inside your existing team month to month.
Enterprise readiness
The security, privacy and governance work that unlocks larger customers.
Evaluation is the product roadmap
An AI product without an evaluation set is guided by anecdotes: the demo that impressed an investor, the customer who complained. Neither tells the team whether a change to a prompt, a model or a retrieval step made the product better or worse across the tasks customers actually run.
We build evaluation sets from real tasks early, score every release against them, gate deployment on the results, and grow the sets from production data with customer permission. Model swaps, prompt changes and pipeline improvements become measured decisions.
Real-task test sets
Built from what customers actually do.
Release gates
No deployment that scores worse.
Grows with usage
Production cases added with permission.
Measured changes
Models and prompts compared on numbers.
Agents with permissions, not just prompts
An agent that can read email, update a CRM, send a document and make a payment is powerful and dangerous in equal measure. The failure modes are known: acting on injected instructions in a document, taking an irreversible step on a misread, running up cost in a loop.
We design agents with explicit tool permissions, review points before irreversible actions, limits on cost and steps, isolation of untrusted content, and logging that shows every decision, so the product can be trusted with real access and the customer can see what it did.
Scoped permissions
Each tool granted deliberately, per customer.
Review before irreversible
Human confirmation where it matters.
Limits
Cost, steps and time bounded.
Full trace
Every decision and action logged.
Enterprise-ready in the UK and Europe
The buyers who pay the most ask the hardest questions. Where is data processed? Which models, under what terms? Is there a DPIA and a record of processing? How is human oversight built in? Which EU AI Act obligations apply? Can it be deployed privately? Products that cannot answer these lose months in procurement.
We build the answers into the product: UK-region and private deployment options, documented data flows and model terms, governance features such as review queues and audit logs, and a documentation pack for security, legal and procurement teams that is maintained as the product changes.
Deployment options
UK region, private cloud or on-premise.
Data flows documented
What goes to which model under which terms.
Governance features
Review queues, audit logs, oversight controls.
Documentation pack
For security, legal and procurement, kept current.
Unit economics from the first customer
Model costs scale with usage in ways that can turn a healthy price into a loss-making customer. A product priced per seat with unbounded model use, or one that retries expensive calls, or one that sends the whole document when a paragraph would do, discovers the problem in the invoice.
We measure cost per task and per customer from the prototype, design routing so cheap models handle what they can, cache and trim what is sent, cap usage per plan, and build the observability that shows margin per customer, so pricing is set on evidence.
Cost per task
Measured from the prototype onward.
Model routing
The cheapest model that meets the quality bar.
Plan caps
Usage bounded per customer and plan.
Margin visible
Cost and revenue per customer in one view.
From prototype to paying customers without a rebuild
Many AI prototypes are built to impress and then thrown away because they cannot be secured, billed or scaled. The second version costs more than the first and loses months. The prototype should be the first slice of the product: the same architecture, the same evaluation harness, the same observability, with the scope cut to one workflow rather than the engineering cut to a demo.
We build prototypes on the production architecture, prove quality on real tasks with a handful of design partners, and extend rather than replace, so the path from prototype to first paying customer is a sequence of releases rather than a restart.
Production architecture
The prototype is the first release, not a throwaway.
Design partners
Real customers on real tasks from the start.
Extend, not restart
Each release builds on the last.
Time to revenue
A sequence of releases to the first paying customer.
AI Product Development & Agents in our other markets
Infynix Solutions is registered separately in India, the United Kingdom and the United Arab Emirates. This page covers United Kingdom. The same service, written for the other markets:
AI Product Development & Agents questions
What is different about building an AI product?
Quality is probabilistic and has to be measured, costs scale with usage, the model is a component that will be replaced, and buyers ask about data, privacy and oversight before they sign. We build the evaluation, cost control, governance and documentation alongside the product.
Which models do you build on?
Whichever suits each task: commercial models through APIs with UK data terms, open-weight models deployed privately, or small specialised models. The architecture keeps them swappable.
Can the product be deployed privately or in the UK?
Yes. UK-region, private cloud and on-premise deployment options are designed in for customers whose data cannot leave their control.
How do you handle UK GDPR and the EU AI Act?
Data flows, lawful basis, DPIA support and human oversight are built into the product and documented; where the product is sold into the EU, the AI Act category is assessed and the obligations designed in.
How do we know the product is accurate?
Evaluation sets built from real tasks score every release, deployment is gated on the results, and production quality is monitored, so accuracy is a number rather than an impression.
Can you build agents that take actions in other systems?
Yes, with scoped permissions, review points before irreversible actions, cost and step limits, protection against injected instructions and a full trace of every decision.
How do you keep model costs under control?
Cost per task is measured from the prototype, cheaper models handle what they can, inputs are trimmed and cached, usage is capped per plan, and margin per customer is visible.
Can you work inside our existing team?
Yes. Embedded AI engineers work in your product team month to month, bringing the evaluation, governance and platform practices with them.
