AI at Versatile Commerce
We build AI products that work on your data
From chatbots that answer from your own history to models that predict what happens next, we design, train and run AI systems inside real products. Our in-house data scientists and machine-learning engineers have shipped AI into platforms used every day, including our own.
Many
AI products shipped into production
In-house
Data scientists and ML engineers
Your data
Models grounded in your own records, never generic answers
Human
Approval and audit trail on every automated action
How we work
Useful, explainable AI, not demos
Every AI system we build starts with your data and your workflow, not with a model. We connect to the records you already have, clean and structure them, and only then choose the technique: a language model, a forecasting model, a vision model or a combination.
We measure before we ship. Each product has an evaluation set, guardrails on what it can and cannot do, and a human-approval step where the stakes are high. The result is AI your team can trust and explain to a customer or an auditor.
What we build with AI
01Agentic workflow automation
AI agents that carry out multi-step tasks across your systems, such as checking an order, confirming stock, drafting the customer reply and raising the ticket, with a person approving before anything is sent.
Learn more- Examples
- Order-exception handling across marketplace, warehouse and helpdesk
- Automated reporting that explains the numbers, not just charts them
- Rules-based automation with explainable, reversible actions
02Prediction and forecasting from past data
Models that learn from your history to tell you what is likely next: demand, churn, delivery times, cash flow or risk. Forecasts come with confidence ranges and the reasons behind them, so planners can act on them.
Learn more- Examples
- Demand forecasting that creates purchase orders ahead of lead times
- Churn and account-health scoring for subscription and SaaS businesses
- Delivery-time and stock-out prediction for multi-warehouse operations
03Document and data intelligence
Extraction, matching and validation of information from invoices, supplier feeds, contracts, forms and scanned documents, so people stop re-keying and duplicates stop entering your systems.
Learn more- Examples
- AI matching of incoming products to existing records to prevent duplicates
- Invoice and purchase-order extraction with validation rules
- Contract and compliance document review with expiry tracking
04Recommendation and pricing engines
Systems that decide what to show and what to charge, in real time, within the rules you set. Built to protect margin and to explain every decision.
Learn more- Examples
- AI repricing that considers margin, competition and your own listings
- Product recommendations and personalised search for commerce
- Next-best-action suggestions for sales and service teams
05AI chatbots grounded in your historical data
Assistants that answer customers and staff from your own documents, tickets, orders and knowledge base, with sources shown for every answer. They run inside your website, app, WhatsApp or helpdesk and hand over to a person when needed.
Learn more- Examples
- Customer support assistant trained on past tickets and policies
- Internal assistant that answers questions about orders, stock and performance in plain English
- Sales assistant that qualifies leads from your CRM history
06Computer vision and speech
Models that read images, video and audio: product photos, documents, shelf and warehouse scans, call recordings and voice notes, turned into structured data your systems can use.
Learn more- Examples
- Image-based product cataloguing and quality checks
- Barcode, label and document capture for warehouse and field teams
- Call and voice-note transcription with summaries and actions
07Advanced AI tooling and platforms
For teams that need more than a feature: retrieval pipelines over large private data, model evaluation and monitoring, fine-tuned models for a domain, and the MLOps to keep them accurate in production.
Learn more- Examples
- Private retrieval over millions of documents with access control
- Fine-tuned domain models with evaluation suites and drift monitoring
- AI governance: audit trails, guardrails and human override built in
Agentic AI, in practice
How we build teams of AI agents, with people in control
An AI agent does more than answer a question: it plans a task, uses tools and systems to carry it out, checks its own work and reports back. For anything beyond a single step we do not build one big agent. We build a small team of specialised agents with a clear hierarchy, and we decide up front exactly where a person must approve before anything happens.
When agentic AI is the right tool
- The task has several steps across more than one system, such as checking an order, confirming stock and replying to a customer
- The steps are known but the inputs vary, so rules alone keep breaking
- Volume is high enough that people spend hours a day on routine handling
- Mistakes are recoverable or can be caught at a review point before they reach a customer or a ledger
The agent hierarchy
Every agent has one job, a fixed set of tools and a limit on what it may do alone. Work flows down the hierarchy and results flow back up, so there is always one place to look to understand what happened and why.
- Person
Business owner
Sets the goal, the budget and the rules. Approves anything the agents are not allowed to do on their own.
- Approval inbox
- Dashboard
- Audit log
- Agent
Orchestrator agent
Breaks the goal into steps, assigns each to a specialist, checks their results against the plan and asks a person when a gate is reached.
- Task planner
- Policy rules
- Gate checks
- Agents
Specialist agents
Each handles one kind of work with only the access it needs: looking things up, analysing data, drafting content or taking an action in a system.
- Research agent
- Data agent
- Drafting agent
- Action agent
- Agent
Reviewer agent
Independently checks specialist output for errors, policy breaches and missing information before the orchestrator accepts it.
- Validation rules
- Evaluation set
- Policy checks
Approval gates that hold
A gate is a point where the agents must stop and wait for a person. We agree the gates with you before any agent is built, and they are enforced in code, not left to the model's judgement.
Every gate decision, approval and rejection is logged with who approved, what the agent proposed and what was actually done. Approvers can edit the proposal before accepting it, and any action can be rolled back.
01 · Money moves
Refunds, payments, price changes and purchase orders above an agreed value always wait for sign-off.
02 · Anything a customer sees
Emails, messages and public listings are drafted by an agent and sent only after a person approves, until trust is earned for a narrow, low-risk category.
03 · Irreversible changes
Deleting records, changing stock levels or updating a contract requires explicit approval, with the before-and-after shown.
04 · Low confidence or a new situation
If the reviewer agent flags doubt, or the case does not match anything seen before, the task is handed to a person with the agent's reasoning attached.
05 · Budget and rate limits
Each agent has a cap on actions, spend and API calls per hour. Hitting a cap pauses the run and alerts a person.
How we implement it
- 01
Map the process
We sit with the people doing the work today and write down every step, system, decision and exception. This becomes the plan the orchestrator follows.
- 02
Design the agent team
We decide which steps become which agents, what tools and data each one gets, and where the gates sit. Access is granted per agent, never shared.
- 03
Build and evaluate
Agents are built against a test set of real past cases. We measure how often they reach the right outcome and how often they correctly stop at a gate.
- 04
Run in shadow mode
The agents work alongside your team without acting, proposing what they would do. You compare and we tune until the proposals are trusted.
- 05
Go live with gates on
Agents act within their limits and queue everything else for approval. Dashboards show throughput, gate decisions and cost in real time.
- 06
Widen autonomy carefully
As categories prove reliable, you choose which ones can skip a gate. Guardrails, logging and rollback stay on permanently.
A worked example: a marketplace order exception
- 1
Orchestrator
Receives a customer message asking to expedite order #2048 and plans the steps.
- 2
Research agent
Looks up the order and the shipping policy and confirms the order qualifies.
- 3
Data agent
Checks stock across warehouses and confirms it can ship tomorrow.
- 4
Drafting agent
Writes the customer reply and the warehouse instruction.
- 5
Reviewer agent
Verifies the order number, the promised date and that the reply follows the tone guide.
- ⏸
Approval gate
The customer-facing reply waits for a person. The approver tweaks one sentence and accepts.
- 7
Action agent
Sends the reply, updates the order priority and logs every step with timestamps.
A team of experienced data scientists
Our in-house data scientists and machine-learning engineers bring deep, hands-on experience in building, training and deploying models that deliver measurable business results. They work alongside our product engineers, so AI arrives as a finished, supported feature rather than a prototype.
- Data science, machine learning and MLOps engineers in one team
- Experience with language models, forecasting, vision and recommendation systems
- ISO 27001:2022 certified handling of your data throughout
From idea to a running AI product
- 01
Discover
We map the decision or task, the data you have and what good looks like, then run a short proof of value on your real records.
- 02
Build and evaluate
We build the model and the product around it, with an evaluation set that tells us honestly how well it works before anyone relies on it.
- 03
Run and improve
We deploy with monitoring, guardrails and human approval, then keep improving the model as your data changes.
Start with your data
Have a problem AI could solve?
Tell us about it. We will tell you honestly whether AI is the right tool, and show you a proof of value on your own data.
Versatile Assistant
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Sent. Thank you!
One of our team will reply by email, usually within one working day. If it's urgent, call +44 333 006 2294.
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