Custom AI for UK businesses that has to survive an ICO question and a board review. We build LLM applications, autonomous agents, computer vision and predictive models into the systems your team already runs, host them in UK regions when the data cannot leave, and work GMT and BST hours from first call to handover.
British businesses sit on claims files, contracts, patient records, product catalogues and call recordings that people still work through by hand. Six service lines cover the job end to end, from the first evaluated prototype to a model your own team operates, with the data protection paperwork written as we go.
Our Services
01 · Service line
LLM & Generative AI Applications
Retrieval-augmented answers over your own documents, with citations
Drafting, summarising and Q&A inside Microsoft 365 and Google Workspace
Evaluation sets and hallucination testing before anyone relies on it
Prompt and token-cost tuning per workload, reviewed monthly
GPT, Claude, Gemini, Llama and Mistral, chosen per task
A demo that impresses in a boardroom is the easy part. The return shows up in the working day afterwards, so here is where custom AI genuinely pays for itself in a British business.
Hours back, every week
The work that quietly eats a UK team is reading, sorting and re-keying. Models take the first pass, and your people handle the exceptions instead of the whole queue.
Post, email and PDFs read, classified and routed
Drafts prepared for review, not typed from scratch
People approve, the model does the keying
Decisions on evidence, not instinct
Forecasting, scoring and anomaly detection turn the history already sitting in Sage, Salesforce or your warehouse into a number someone can act on this week.
Cash-flow and demand forecasts from your own ledger
Lead and churn scores that rank honestly
Anomalies flagged before month-end, not after
Systems that survive Monday morning
A pilot that works on one laptop and a system that runs at 8am on a Monday are different builds, and we design for the second one from day one.
A test set that says how good it really is
Drift watched and retraining scheduled
Spend and latency tracked per workload
A service customers notice
Answers in seconds instead of a ticket queue, and personalisation based on what a customer actually did rather than the segment they were dropped into.
Customers answered at 11pm without a rota
Recommendations based on what people actually bought
AI-led service tiers your competitors cannot copy quickly
AI That Pays for Itself Inside the Working Day
A chatbot on the website is not a transformation programme. These six disciplines are how we turn the data a UK business already holds into decisions it can defend, with our AI development and cloud teams behind them. If you need agents that act rather than answer, start with AI agent development.
[1]Retrieval-Augmented Generation
Every answer is pulled from your own policies, contracts and records and shown with its source, so the model cannot invent a clause or a price that was never written down.
[2]Autonomous AI Agents
An agent works through a task step by step, calling your CRM, ERP or helpdesk as it goes, and stops for a named person to approve anything that moves money or leaves the building.
[3]Predictive Modelling
Forecasts, scores and rankings trained on your own history, always measured against the simplest rule a finance director would have used instead, so the uplift is a number and not a claim.
[4]Computer Vision
Reading photographs, scans, drawings and video the way your best claims handler or inspector does, running on site when the connection is poor or the answer is needed in seconds.
[5]Conversational & Voice AI
Chat and telephone assistants that speak natural British English, answer only from what your knowledge base actually says, and pass the conversation to a colleague with the context intact.
[6]MLOps & Evaluation
Prompt and model versions tracked, drift spotted before customers notice, retraining on a schedule and a cost dashboard, so nobody has to guess whether the system got worse this month.
Technologies
The AI Stack We Build On for UK Teams
Seven techniques that separate a model that sounds convincing from one a UK regulator, auditor or customer can rely on. Choose one to see where it belongs in a build.
Large Language Models
GPT, Claude, Gemini, Llama and Mistral are all on the bench, and the one that wins is decided by a benchmark on your own tasks rather than by a press release. Quite often a smaller model clears the bar at a tenth of the cost per call, and we say so when it does.
Retrieval & Vector Search
Contracts, policies, tickets and internal wikis indexed by meaning instead of keyword, so the model is handed the passages that matter and cites them back. We build on pgvector, Pinecone or OpenSearch according to where your data already sits, not where a vendor would like it to.
AI Governance & Guardrails
Filters on what goes in and what comes out, personal data redacted before it reaches a third-party API, refusals handled gracefully and every prompt and response logged. Under UK GDPR and the ICO's AI guidance that log is how you answer a subject access request without guessing.
Fine-Tuning & Distillation
Fine-tuning on your labelled examples, or distilling a large model into a small one that runs cheaply in a UK region, comes last on our list rather than first. Retrieval and better prompting usually get there for less, so we exhaust those before touching weights.
Evaluation & Observability
A test set held back from the build, scored automatically, with a regression check on every change to a prompt or model. It turns the question of whether the system got better into a figure on a dashboard rather than a debate about how last week felt.
Vision & Multimodal Models
Models that read photographs, video and scanned paper as readily as they read text. In UK businesses that tends to mean claims photographs, planning drawings, shelf footage and the paper forms nobody has time to key in.
Private & Self-Hosted Deployment
Open-weight models running inside your own VPC in Azure UK South or AWS London, so regulated data never leaves the region. The right choice for NHS, FCA-regulated and public-sector work, and for anyone whose data processing agreement says so.
Thinking About AI? Start With a Free Use-Case Audit.
One call about your data, your workflow and what you are trying to change tells you which use case is worth funding first, what it will realistically cost in pounds and where AI is the wrong tool. So your first budget line goes somewhere that pays back.
Free · No obligation · Delivered within 48 hours, UK time
Industries
AI Built for UK Industries
Nine sectors where British businesses are already putting models to work, from FCA-regulated lenders to NHS trusts. See our fintech and healthcare practices for deeper detail, or enterprise software when the AI has to reach the rest of the business.
[01]Financial Services & FinTech
Underwriting, fraud and Consumer Duty work where an audit trail and an explanation are not negotiable.
Delivered since 2018 across AI, mobile, cloud and enterprise builds.
70%
Front-desk call load removed
Reduction in calls reaching the front desk after the healthcare voice agent we built went live, answering around the clock.
78%
Queries resolved automatically
Share of customer questions the WhatsApp assistant we built for an e-commerce client resolves without a person.
15+
Countries served
Client operations running on Appther-built platforms worldwide, including the UK and Europe.
Case Studies
Work We've Shipped
Three AI builds we can point at: a voice agent answering every patient call, a WhatsApp assistant carrying frontline support, and a routing engine trained on live fleet data.
Why UK Teams Choose Appther for Custom AI Development?
Any agency can demo a chatbot. Far fewer will run your own data through a test set in the first week, tell you plainly what a model can and cannot do, and put the data protection paperwork in the plan rather than the appendix. We cover the whole stack in house, with our AI development and cloud infrastructure teams on the same roadmap.
01
One team, whole stack
Pipeline, retrieval, model, interface and the APIs your other systems call are built by one team on one roadmap, so when an answer goes wrong there is nobody to point at but us.
02
Measured, not demoed
Nothing ships without a held-back test set and a plain baseline to beat. When the simple rule does as well as the model, you hear that from us before you pay for the model.
03
Compliance in the plan, not the appendix
Lawful basis, a data protection impact assessment where one is needed, UK-region hosting and an audit log are scoped in discovery, so your DPO signs off before the build rather than after it, backed by our AI engineering practice.
04
Fixed scope, fixed price in pounds
Discovery ends with a written scope and a fixed quote in pounds. You own 100 per cent of the code, prompts, fine-tuned weights and documentation, with an NDA signed up front.
Security & Compliance
How We Protect Your Data in an AI Build
Training and prompting put your data somewhere new, and in the UK that carries obligations to the ICO, to your customers and often to a sector regulator. This is how we treat each category, limited to what we can honestly claim.
Personal data turns up in prompts and training sets far more often than teams expect. We start from minimisation, so UK GDPR compliance is a property of how the system is built rather than something bolted on before launch.
UK GDPR & DPA 2018Lawful basis mapped per feature, a DPIA where automated decisions affect people, retention rules enforced in code and ICO breach timelines rehearsed, not just documented.
EU GDPR for EU usersWhere you serve EU customers, transfer mechanisms and EU representative obligations are covered, and the model behaves the same on both sides of the Channel.
EU AI ActSystems classified by risk tier, with the transparency, logging and human-oversight obligations for your tier documented before the build starts.
Data minimisationWe send the model the least data that still answers the question, and redact personal data before it reaches a third-party API, which also cuts token cost.
The weakest route into a model is the one that gets used. Identity, transport, secrets and prompt handling are designed as a single system rather than three features that were each somebody else's job.
Tenant isolationEach tenant gets its own keys and its own namespace in the vector store, so a retrieval query for one customer cannot surface another customer's documents, by construction rather than by policy.
Encrypted transportEvery call to a model travels over TLS, secrets live in a managed vault, and nothing in a prompt or response is written to a log outside your tenancy or outside a UK region.
Prompt-injection defenceAnything a user uploads or a website returns is treated as data, never as an instruction, and every tool call is checked against a schema so a booby-trapped PDF cannot act as if it were staff.
Least-privilege toolsAn agent receives the smallest set of permissions that still gets the job done. Spending money, sending email or deleting records all wait for a named person to approve.
These are the engineering practices we apply on every UK project. Appther is ISO/IEC 27001 and ISO 9001:2015 certified, and the same practices are aligned with Cyber Essentials.
OWASP Top 10 for LLMsEvery release is checked against the OWASP list for LLM applications, from prompt injection and insecure output handling through to excessive agency.
ISO/IEC 27001 certifiedAccess control, change management and incident response run under Appther's ISO/IEC 27001-certified information security management system, whether or not your organisation holds the certificate yet.
Secure coding & reviewNothing merges without a second engineer reading it, static analysis and dependency scanning run in CI, and the repository holds no secrets.
Penetration testingFor regulated workloads an independent tester is booked before launch, and every finding is fixed and retested before anything is disclosed.
Appther's ISO certifications cover how we work; they do not certify your product. ISO 27001, Cyber Essentials Plus or DTAC attestation of your finished product sits with your compliance team and its assessor. Our job is to build and document so that the assessment is short.
A regulated sector changes the shape of the architecture, not just the paperwork around it. We scope these constraints in discovery so they never turn up in month four as a surprise.
NHS DTAC-ready buildsFor NHS and clinical workloads: DTAC evidence, DCB0129 clinical safety documentation, patient data kept in UK regions and no PHI sent to endpoints outside your agreement.
FCA Consumer DutyDecisions that affect a customer's outcome are logged with their inputs, so you can explain to the FCA what the model recommended and why.
PCI DSS scope reductionCard data redacted before it reaches any model, so the AI layer stays out of scope and your assessor's job stays small.
UK data residencyAzure UK South, AWS London or in-tenancy hosting for prompts, embeddings and logs, documented for your DPO and any regulator who asks.
AI products lose trust and fail public-sector review for predictable reasons. We build against the specific rules and expectations that catch AI features out, so yours passes the first time.
Disclosure of AI useUsers are told when they are dealing with a model and can reach a person without having to argue with it first, which is what the ICO and the FCA both expect to see.
Human escalation pathsEvery assistant we ship has a written route to a human, and the model is built to take it when confidence drops rather than bluff its way through.
WCAG 2.2 AAContrast, focus order and screen-reader behaviour are tested against WCAG 2.2 AA, meeting the Public Sector Bodies Accessibility Regulations where they apply.
Failure statesAn unsure model says it is unsure and shows where its answer came from, rather than replying with a confidence it has not earned.
Grouped by the layer each tool belongs to, from the model that answers to the pipeline that keeps it honest. Every one of them can run inside a UK region when your data requires it.
OpenAI
Anthropic Claude
Google Gemini
Meta Llama
Mistral
DeepSeek
Qwen
Hugging Face
LangChain
LangGraph
CrewAI
PyTorch
TensorFlow
FastAPI
Python
Node.js
pgvector
Milvus
Qdrant
Redis
MongoDB
TimescaleDB
InfluxDB
Grafana
AWS Bedrock
Azure OpenAI
Google Vertex AI
Docker
Kubernetes
Terraform
React
Twilio
Feature Set
Features We Ship in AI Applications
The building blocks that turn up in most AI platforms we deliver for UK teams, from the first retrieval query through to production monitoring.
Document Q&A Over Your Own Data
Autonomous Task Agents
Scheduled Model Retraining
Streaming Responses & Live Transcription
Forecasting & Churn Prediction
Model Quality & Cost Dashboards
End-to-End Encryption
Per-Tenant Isolation & Access Control
Multi-Model Routing (GPT, Claude, Gemini, Llama)
ERP, CRM & Helpdesk Integrations
Role-Based Access Control
Alerts & Human Escalation
Prompt & Model Versioning
Computer Vision & OCR
Evaluation & Drift Analytics
Token & Inference Cost Tuning
Architecture
AI Architecture, Layer by Layer
Ten layers make up a production AI system, and each one has to agree with the layers either side of it. A mistake in one shows up somewhere else, usually as an answer nobody trusts, so we design the ten as a whole rather than one after another.
Data Sources
Where the answers have to come from: the CRM, the document store, the warehouse, the ticket history and the folder on the shared drive that everyone still uses.
01
Ingestion & Cleaning
Parsing, de-duplication and chunking. A disappointing AI project is usually a data problem that has dressed up as a model problem.
02
Embeddings
Text, images and audio converted into vectors, so what the model is shown is decided by meaning rather than by matching keywords.
03
Vector Store
pgvector, Qdrant or Milvus, sized to your corpus and partitioned by tenant, so one customer's retrieval can never wander into another's documents.
04
Retrieval
Hybrid search and re-ranking that place the right passages in front of the model, with the citation carried all the way through to the answer.
05
Model Layer
The model itself, hosted or self-run in a UK region, picked for each task on cost, latency and accuracy rather than on reputation.
06
Orchestration
Prompts, tools, memory and multi-step plans: the layer where an agent decides what to do next and which of your systems it is permitted to touch.
07
Guardrails
Filtering on input and output, personal data redacted, refusals handled, and an audit log that lets you answer a subject access request truthfully.
08
APIs & Integration
The answer delivered into Salesforce, Dynamics, the helpdesk or your own product, because a model that lives behind a separate login is opened twice and then forgotten.
09
Evaluation & Monitoring
Held-back test sets, drift detection and a cost dashboard, so quality stays a number you can watch rather than a feeling you have.
10
Data Sources
Where the answers have to come from: the CRM, the document store, the warehouse, the ticket history and the folder on the shared drive that everyone still uses.
01
Ingestion & Cleaning
Parsing, de-duplication and chunking. A disappointing AI project is usually a data problem that has dressed up as a model problem.
02
Embeddings
Text, images and audio converted into vectors, so what the model is shown is decided by meaning rather than by matching keywords.
03
Vector Store
pgvector, Qdrant or Milvus, sized to your corpus and partitioned by tenant, so one customer's retrieval can never wander into another's documents.
04
Retrieval
Hybrid search and re-ranking that place the right passages in front of the model, with the citation carried all the way through to the answer.
05
Model Layer
The model itself, hosted or self-run in a UK region, picked for each task on cost, latency and accuracy rather than on reputation.
06
Orchestration
Prompts, tools, memory and multi-step plans: the layer where an agent decides what to do next and which of your systems it is permitted to touch.
07
Guardrails
Filtering on input and output, personal data redacted, refusals handled, and an audit log that lets you answer a subject access request truthfully.
08
APIs & Integration
The answer delivered into Salesforce, Dynamics, the helpdesk or your own product, because a model that lives behind a separate login is opened twice and then forgotten.
09
Evaluation & Monitoring
Held-back test sets, drift detection and a cost dashboard, so quality stays a number you can watch rather than a feeling you have.
Your data, your latency budget and your accuracy bar decide which of these fits, not what is trending this quarter. Our AI agent development and AI development services pages cover the layers on either side of the model.
Our Process
How We Deliver AI Projects in the UK
From the first conversation to a system your own team operates, the work runs through thirteen stages. Model work punishes guesswork, so the risky parts are proven on your data early, the data protection file grows alongside the build, and every stage ends with a result you can measure.
Week 1
Discovery
The use case, its constraints and what success means, settled before any model or platform is named.
1
What we doWe sit with the people who do the work now, trace where the hours really go, and check whether your data can carry the use case at all. Existing tools, contracts and processor agreements are reviewed at this point rather than discovered later.
What you getA written problem statement, the single metric that decides success, and a shortlist of use cases ranked by return rather than by novelty.
Week 1–2
Business Case
What the AI has to be worth, in pounds, before it is worth building.
2
What we doWe put a figure on the hours or revenue the deployment is meant to produce, size the token and infrastructure spend at launch and at scale, and agree the number we will be judged against.
What you getA business case with the arithmetic shown, and a scope you can hold us to.
Week 2–3
Data Readiness
A candid look at whether your data can support the use case.
3
What we doCoverage, labelling, freshness and access rights are audited across every system involved, and we identify what must be cleaned, joined or collected before a model can be trusted. The lawful basis for each source is confirmed here.
What you getA data assessment, a cleaning plan and a plain statement of what should be built first.
Week 3–5
Prototype & Evaluation
A working prototype scored against a baseline, not a demo video.
4
What we doWe build the simplest thing that could work, often retrieval plus a good prompt, and score it on a held-back set from your own data against a plain rules-based baseline. Sometimes the baseline wins, and we tell you.
What you getA prototype running on your data, a measured accuracy figure and a go or no-go decision you can act on.
Week 4–7
Model Selection
The model chosen on cost, latency and accuracy for your tasks, not its reputation.
5
What we doCandidate models, hosted and open-weight, are benchmarked on the tasks from stage four. We weigh residency, per-token cost at your volume and the price of being wrong, and trial self-hosting in a UK region where the data demands it.
What you getA documented model decision with the figures behind it, including the cost per thousand requests you should expect.
Week 5–10
Retrieval & Data Pipeline
The plumbing that decides what the model is allowed to see.
6
What we doChunking, embedding, hybrid search and re-ranking are built over your document sources, partitioned by tenant so one customer's retrieval can never reach another's records.
What you getA retrieval layer that cites its sources, an ingestion pipeline your team can re-run, and a corpus you can grow without us.
Week 7–13
Orchestration & Agents
Prompts, tools and multi-step plans connected to your real systems.
7
What we doWe define which tools the agent may call, which actions need a person's sign-off and how memory and context are managed, then connect it to your CRM, helpdesk or ERP through supported APIs.
What you getWorking agent flows with approval gates, and an audit log of every action the agent has taken.
Week 8–14
Guardrails & Compliance
The safety layer, designed in from the start rather than bolted on at the end.
8
What we doFiltering on input and output, personal-data redaction, refusal handling and rate limits, each mapped to UK GDPR, the ICO's AI guidance, any sector rules and your own data processing agreements.
What you getA documented guardrail set, a DPIA where one is needed, and a compliance file your DPO can answer questions from.
Week 9–16
Integration
The output delivered into the tools your team already has open.
9
What we doWe connect to Salesforce, Dynamics, HubSpot, Odoo, Zendesk, Sage or your own product through supported APIs, so the answer arrives where the work happens instead of behind another login.
What you getAI inside the daily workflow, in systems people already use, so it is opened more than twice.
Week 10–17
Interface & UX
The screen your people will actually use every day.
10
What we doWe design for the moment the model is unsure: sources shown, a hand-off offered and the escalation route obvious. Then we test it with the people who will use it, not the people who commissioned it.
What you getAn interface people trust, accessible to WCAG 2.2 AA, and still in daily use by week six.
Week 14–20
Evaluation & Hardening
Proof that it holds up before anyone relies on it.
11
What we doRegression tests run on every prompt and model change, red-team prompts probe for injection and leakage, load tests run at realistic volume, and a penetration test is booked where the workload is regulated.
What you getA quality baseline to measure against, and a system that behaves under load and under attack.
Week 18–22
Deployment
Production rollout, staged so a bad change never reaches everyone at once.
12
What we doA canary release behind feature flags in your own cloud account, in a UK region where residency applies, with observability on latency, cost and quality and a rehearsed rollback.
What you getA live system, runbooks and dashboards, and a team of yours that knows how to run it.
Ongoing
Monitoring & Retraining
The years after launch, where most of an AI system's life is actually spent.
13
What we doWe watch for drift as your data and your customers change, retrain on a schedule or a trigger, and re-run the evaluation set whenever a new model version is released.
What you getQuality that holds over time, costs that stay predictable, and a monthly report your board can read.
Client Voices
What Clients Say After Go-Live
“Appther built our student competition app exactly the way we envisioned it: easy registration, smooth judging, and real-time results that keep participants engaged.”
Dionna MilemOwner · My BOB Team LLC
“Building a mental health app means every detail has to be handled with care, and Appther got that from day one. The product feels warm, simple, and genuinely helpful.”
Bob SinghalProfessor of Joy · Joyscore Inc
“Appther exceeded every expectation, on time and on budget, and the final product has been instrumental in scaling our business. A rare engineering partner.”
Johnathan PefferCEO · Bull's Eye Technologies Inc
“Implementing an ERP across our operations felt daunting at first, but Appther made the transition seamless. Every department now works off a single source of truth.”
Pushpender KumarGeneral Manager · AB Sugar Ltd
Latest Insights
Guides for UK AI Buyers
Architecture, cost and integration guides written from real builds, for the questions British teams ask before they fund one.
Where models earn their place inside Sage, Odoo, Dynamics and NetSuite, and where they do not.
Free 30-Minute AI Strategy Call, UK Hours
Your AI Roadmap Starts With One Call. Make It This Week.
One call leaves you with a prioritised use case, a realistic budget in pounds and a delivery timeline. No slide deck, no obligation, and a straight answer if AI turns out to be the wrong tool.
Prioritised use case Budget in pounds Delivery timeline
It depends on scope. A focused pilot, one use case with a working model and a live integration, usually lands between £15,000 and £40,000. Production platforms with several models, MLOps and enterprise integrations run from £60,000 upwards. You get a fixed-price quote in pounds after a free scoping call, with no open-ended day rates.
A proof of concept running on your own data takes four to six weeks. A production system with monitoring, retraining and integrations lands in three to six months, and the biggest variable is how ready your data is rather than which model you pick. Data readiness is stage three of our process for exactly that reason.
Yes. Project hours are set to GMT and BST, so stand-ups, demos and escalations happen inside your working day. Delivery runs as a distributed team from Noida, India, with a named senior engineer as your point of contact. Appther has no UK office; our registered entities are in the United States, India and Australia, and we say so rather than imply otherwise.
GPT, Claude, Gemini, Llama, Mistral and open-weight models, hosted through Azure OpenAI, AWS Bedrock, Google Vertex or your own tenancy. Choice is made per workload by benchmarking cost, latency and accuracy on your tasks, and we will tell you when a smaller model does the same job for a fraction of the spend.
Lawful basis, data minimisation and retention are mapped per feature during discovery, with a data protection impact assessment where automated decisions affect people. We follow the ICO's guidance on AI and data protection, and where you serve EU users we classify the system under the EU AI Act and document the obligations that follow. The result is a compliance file your DPO can hand to a regulator.
Yes. When residency matters we deploy in Azure UK South, AWS London or Google Cloud London, inside your own account, with open-weight models self-hosted so prompts and documents never leave the region. When it does not, hosted APIs are cheaper and faster to launch. We recommend the option that fits your obligations, not the one that is easiest for us.
Yes, and that is usually where the return is. We connect models to Salesforce, HubSpot, Dynamics, Odoo, Zendesk, Sage, Xero and in-house systems through supported APIs, so the answer lands inside the tool your team already opens rather than behind a separate login.
It depends on your data sensitivity, volume and latency budget. Commercial APIs win on speed to launch and quality per pound at low volume. Open-weight models win when data cannot leave your tenancy, when volume makes per-token pricing expensive, or when you need to fine-tune. Most UK builds end up using both, routed by task.
Yes. You own 100 per cent of the code, prompts, evaluation sets, fine-tuned weights and documentation, with an NDA signed up front and IP assigned in the contract. Everything runs in your own cloud accounts, so nothing depends on us if you choose to take it in-house.
Financial services and fintech, NHS and private healthcare, retail and e-commerce, professional and legal services, manufacturing, logistics, insurance, property and education. The common thread is a document or conversation backlog that people are still working through by hand, and a regulator who will ask how the model reached its answer.
An AI Engineer Replies Within One Business Day, UK Hours
Let's Scope Your AI Project
Tell us what you want to automate, predict or answer, and which systems hold the data. No sales pitch, just a straight answer on approach, scope, timeline and cost in pounds. Prefer email? Use our contact page.
Free 30-min strategy callBooked on UK time, with plain advice on what to fund first.
Fixed-price quote in poundsWritten scope and price before the build starts, no day-rate drift.
NDA on requestYour data, prompts and roadmap stay yours.
