Automation
Triage, drafting, data entry and document handling move to the model, with your people left on the exceptions rather than the queue.
GPT integration services
Put a foundation model to work inside the systems you already run — connected to your data, your tools and your processes, rather than sitting beside them in a chat window.
Model-agnosticFunction calling and retrievalScope limits by default
Benefits
What integration actually changes
The four effects that show up first when a model is wired into real processes rather than piloted beside them.
Cost optimisation
Labour-intensive steps across sales, support and back office get cheaper per unit, and the saving compounds as volume grows.
Process reinvention
Once retrieval and function calling are in place, a process can be re-cut rather than merely sped up — which is usually where the larger gain sits.
Personalisation at scale
Replies shaped by the customer record and history, on every channel, at a volume no team could reasonably staff.
Impact
Typical numbers from a GPT rollout
Ranges we see across engagements. [VERIFY these before publishing — they are indicative third-party figures carried over from the reference material, not audited client results.]
Of routine support tickets closed without a person in the loop.
Indicative rangeMore enquiries handled per agent after a copilot rollout, with no extra headcount.
Indicative rangeAverage order value where recommendations are generated from live customer data.
Indicative rangeTime spent on first drafts of routine written work.
Indicative rangeOur services
Our GPT integration services
A full suite of integration services, sized so generative AI becomes something your business can act on. Most engagements use two or three of these, not all four.
Generative AI consulting
Our engineers work with NLP and foundation models daily, which mostly shows up as knowing where generative AI belongs in a business and where it does not. We map the case, look hard at your data, and plan the solution on whichever leading foundation model actually suits it.
- Integration case analysis
- Proof of concept
- Data analysis
- Project estimation and planning
AI and GPT API integration into existing systems
We bring model capability into the software you already run, through APIs, so an existing application gains functionality instead of being rebuilt around a new tool. The integration is designed to be unremarkable from the user's side — the feature is simply there.
- Feasibility analysis
- Integration planning and prototyping
- Integration and testing
- Maintenance and improvement
Model fine-tuning
Where an off-the-shelf model plateaus, we tune it on your material to work past its limits and get full value for your specific case. Training runs against a curated set, and the result is measured against the base model rather than assumed to be better.
- Large language model fine-tuning
- API integration
- Data architecture modernisation
- Cloud migration
Custom solution development based on GPT
Our machine learning engineers build services on top of the model for your enterprise applications, with generative AI in the architecture rather than bolted on afterwards — which is what makes the heavier data processing work reliable.
- Custom GPT solutions development
- Customer service assistants
- NLP software development
- Business automation solutions
Approach
How we integrate GPT
Our developers pick the approach that fully covers the requirement — and say so when the lightest one is enough.
Consume
Use the language model as it ships, through the provider APIs, and get value from easy-to-access technology straight away.
Our developers help you adapt the model and harness it for your own use cases through prompt tuning and prefix learning.
Customise
Get the most out of a language model by fine-tuning it on curated enterprise data rather than prompting around its gaps.
Our engineers tune the model for your specific downstream tasks and bring it into line with your business semantics, internal knowledge and methods.
Orchestrate
Connect the model to your internal tools, data and systems so that it can act, not only respond.
Our engineers build agent-driven workflows that combine function calling, retrieval and automation — turning a chat interface into a working part of your business processes.
Not sure which approach fits your project?
Use cases
Where GPT integration pays for itself
Six patterns that account for most of the work we are asked to do.
Workflow agents
Multi-step tasks completed end to end — routing requests, updating records, raising approvals — with a human kept on the decisions that need one.
Internal knowledge assistant
Employees get answers drawn from your own documents, wikis and databases instead of searching four systems in sequence.
Sales and CRM copilot
Lead research, record enrichment and personalised outreach drafted automatically, so selling time is not spent on data entry.
Virtual assistants
Scheduling, order tracking and routine enquiries handled around the clock without adding headcount to cover the hours.
Content at scale
Marketing copy, product descriptions and localised variants produced in a fraction of the time a team would need to write them by hand.
Document intelligence
Contracts summarised, key fields extracted from reports, and sentiment surfaced from feedback through one pipeline rather than three tools.
Why us
Why teams work with InfuseAI
Scope limits by default
Every assistant ships with a constraint layer, so it answers inside its remit and declines gracefully outside it.
Evaluated, not asserted
An eval harness comes with the build, so quality regressions surface before your users find them.
Senior people, directly
Senior NLP and ML engineers on the work rather than a junior team behind an account manager.
Model-agnostic
GPT, Claude, Mistral or open weights — chosen on cost and fit, with the reasoning shown.
Related
Other ways we can help
Start here
Tell us the problem. We'll tell you if it's worth solving.
Thirty minutes with an engineer, not a salesperson. You leave with a rough scope, a cost band and an honest read on feasibility.
30 minutes with an engineer · No slide deck · Reply within one business day