We build custom AI systems, LLM integrations, computer vision, and predictive analytics for Edinburgh businesses, in a city with one of the oldest and most respected AI research programmes anywhere in the world. Strategy, model development, integration, and monitoring stay with a single team throughout, never split across vendors.
Built by a Team That Ships AI Products, Not Just Demos
Edinburgh's AI research reputation runs deeper than most people realise, built on decades of work that shaped how the field teaches AI itself. Every Edinburgh AI build runs entirely in-house, from discovery through deployment and monitoring.
A Roadmap You Can Set a Watch By
We build production-grade AI systems for Edinburgh businesses. Every engagement follows a milestone-driven plan, and data readiness gets checked honestly before that plan is even finalised.
GMT and BST Hours, Genuinely Covered
Our teams keep dedicated India-UK overlap hours structured around GMT and BST, so Edinburgh clients stay connected through Slack, Jira, and GitHub for the length of the engagement.
Built for Explainability, Not Just Accuracy
We design AI systems that can be audited and explained, not just ones that score well on a benchmark, because a model nobody can explain is a liability for an Edinburgh business, not an asset.
Ratings That Hold Up Past Edinburgh
Clutch and Google scores are earned across AI, mobile, and commerce work, delivered by engineers who build in RAG pipelines, computer vision, and production LLM integrations daily.
Platform Ratings
What our clients say across leading platforms.
Google, 4.9 out of five stars
4.9★★★★★★★★★★
Clutch, 5.0 out of five stars
5.0★★★★★★★★★★
GoodFirms, 4.8 out of five stars
4.8★★★★★★★★★★
What clients love about working with us
Milestones That Land When Promised
Every AI engagement stays on schedule through milestone tracking and sprint accountability, with progress visible the whole way through.
Nothing Gets Buried in an Inbox
Planning, model reviews, and deployment happen live during dedicated overlap hours, not left sitting unread in a Slack channel.
Trusted With Real Production Data
Every system gets a documented architecture and data-handling standards defined upfront, never reverse-engineered after an incident.
A Long-Term AI Partner, Not a Vendor
Clients tend to stay on well past launch, because an unmonitored model drifts quietly, and the whole approach is built around that reality from day one.
Why Edinburgh Businesses Choose Akoode
The University of Edinburgh's Bayes Centre, alongside the Edinburgh Laboratory for Integrated AI and the National Robotarium, anchors one of the UK's oldest and most reputable AI research communities, backed by a £750 million government investment in Edinburgh's own supercomputing capacity. We hold every Edinburgh build to that same standard of research depth.
GMT and BST overlap. Our India-UK delivery model is structured to provide dedicated overlap during Edinburgh business hours for sprint planning, model reviews, and deployment.
Pricing in GBP. We scope and bill entirely in sterling, so there's no currency question at any point.
Built entirely in-house, every model and pipeline the work of our own team.
The same senior engineer follows this build end to end, discovery all the way to deployment.
We build with UK GDPR and the Data Protection Act 2018 in mind from the outset, looping in your legal team wherever formal compliance sign-off is required.
Working Hours Built Around GMT and BST
Structured India-UK overlap windows keep planning, model reviews, and deployment calls landing inside Edinburgh's own working day.
Built With AI Technology Chosen to Last
We choose frameworks, vector databases, and orchestration tools for production reliability, not leaderboard performance. That keeps an Edinburgh AI system maintainable years after launch, not just at release.
Sector Depth Beyond a Single Vertical
Deep delivery experience across Edinburgh's dominant industries: Finance & Banking, Energy & Utilities, Public Sector & Government.
Support That Doesn't End at Deployment
Model monitoring, drift detection, and retraining continue after launch, so an Edinburgh AI system stays accurate as real-world data shifts, not just on demo day.
The Six Things an Edinburgh AI Engagement Actually Covers
Whether the client is a two-person Edinburgh startup testing a first AI feature or an enterprise retooling a core workflow, every engagement covers the same ground: strategy, data readiness, model development, integration, and support that continues past launch day. Budget somewhere between £8,000 for an API integration and £75,000-plus for enterprise or fine-tuned work, based on current UK market pricing.
01
AI Strategy and Discovery for Edinburgh Businesses
Most AI projects that go wrong go wrong before a single model gets trained, in the gap between what an Edinburgh business wants and what its actual data can support. We begin with a candid check on whether the data actually supports the ambition, not a roadmap that's quietly just a sales pitch.
Data readiness and quality assessment before any commitment is made
Use-case prioritisation based on real business impact, not novelty
Build-versus-buy analysis for off-the-shelf APIs versus custom models
A scoped technical roadmap with realistic milestones
Custom AI and Machine Learning Development in Edinburgh
When an off-the-shelf API can't do what an Edinburgh business actually needs, we build custom models: classification, prediction, and recommendation systems trained on real data, not a generic public dataset.
Custom classification, prediction, and recommendation models
Model training, validation, and performance benchmarking
MLOps pipelines for retraining as new data arrives
Explainability built in from the architecture stage, not bolted on
Generative AI and LLM Integration for Edinburgh Teams
We integrate LLMs into real business workflows for Edinburgh teams, RAG pipelines grounded in a client's own documents, AI assistants that actually know the business, and content generation tools that don't hallucinate their way into a compliance problem.
RAG pipelines grounded in your own documents and data
In-app AI assistants using OpenAI, Anthropic, and open-source models
Prompt engineering and evaluation frameworks, not guesswork
Guardrails and output validation built in from day one
Computer Vision Development for Edinburgh Businesses
From defect detection on a production line to document processing in a back office, we build computer vision systems for Edinburgh businesses trained on real operational images, not stock photo datasets that fall apart in production.
Object detection, classification, and image segmentation
Document processing and optical character recognition
Quality inspection and defect detection for manufacturing
Real-time video analysis for production environments
AI-Powered Automation and Predictive Analytics in Edinburgh
We build predictive models and automation that actually change how an Edinburgh business operates, demand forecasting, anomaly detection, and workflow automation grounded in real historical data, not a dashboard nobody acts on.
Demand forecasting and anomaly detection models
Workflow automation triggered by predictive signals
Recommendation engines tuned to real customer behaviour
Dashboards built for decisions, not just reporting
A model that isn't monitored drifts, quietly, until it's making bad decisions nobody notices until the damage is done. We provide ongoing monitoring, retraining, and performance tracking so an Edinburgh AI system stays accurate as real-world data changes.
Model performance monitoring and drift detection
Scheduled retraining as new data becomes available
Cost and latency optimisation for production inference
Flexible staff augmentation when an in-house team needs extra AI capacity
How an AI Project Gets Built in Edinburgh, Step by Step
Six stages that keep every Edinburgh AI engagement transparent and accountable, from the first data conversation through deployment and beyond.
Stage 01
Discovery and Data Assessment
Every Edinburgh engagement starts with a genuine assessment of what data actually exists and what state it's actually in, not an assumption that the data is ready simply because someone said it was.
Timeline
1 to 3 weeks
Most engagements find this stage runs longer than expected, because an honest data assessment genuinely takes time to do properly.
You receive
Data readiness and quality report
Use-case feasibility assessment
Technical scope document with realistic milestones
Risk register covering data, compliance, and integration risks
What'sActually Running Underneath an Edinburgh AI Build
What's here is chosen for durability in production, never for a leaderboard placement. OpenAI and Anthropic cover most generative needs adequately on their own. PyTorch and TensorFlow get reserved for genuinely bespoke model work.
PyTorch
TensorFlow
scikit-learn
HuggingFace
Results We're Happy to Show You
Real AI projects with results attached, model performance, adoption, and business impact, not a demo reel.
AI Player Performance Tracking Case Study
Key Outcomes
10x
Faster Coaching
94%
Tracking Accuracy
Challenge
Performance coaching at the elite level demands data granularity that traditional video review simply cannot deliver. Coaching teams were spending enormous amounts of time rewatching unstructured footage, drawing conclusions by observation, and making player evaluation decisions without a single objective metric to support them. The problem was not effort. It was the absence of the right system.
What We Built
The client needed a next-generation AI system that could take raw, unstructured game footage and turn it into structured, real-time performance intelligence that coaching staff could act on immediately. Every objective defined at the start of this project was tied directly to a coaching workflow problem that needed solving.
The creative production bottleneck in ecommerce and B2B marketing is not a talent problem. It is a process problem. Every product needs multiple ad formats. Every format needs channel-appropriate copy. Every piece of copy needs to stay on-brand across a growing catalogue. When that work is done manually, it does not scale, it does not stay consistent, and it cannot be reviewed efficiently when the output looks different every time a different person touched it.
What We Built
The brief required a modular AI pipeline that could take a product image, understand what it was selling and to whom, generate structured marketing copy across multiple formats, and render downloadable catalogue assets that looked the same every time. Deterministic output was non-negotiable. The system needed to produce layouts a marketing team could review, approve, and send without visual surprises or format inconsistencies between runs.
High-ticket real estate buyers do not convert the way e-commerce shoppers do. They research for weeks, visit multiple platforms, talk to multiple agents, and still leave most sites without taking any action. The platforms dominating Indian real estate search are built for discovery at scale, not for decision support at depth. For a consultancy where the average transaction involves crores, a website that shows listings and a contact form is not a business asset. It is a missed opportunity.
What We Built
The brief was not to build a listings website. It was to build a digital advisory platform where AI handled early buyer guidance, WhatsApp handled lead conversion, and the listings engine handled discovery. Every feature was mapped to a specific moment in the buyer journey where the previous experience was creating friction or losing the conversation entirely.
For healthcare providers in Edinburgh, AI systems built around real clinical decision-support needs, with data handling designed to NHS-aligned standards.
AI systems built to the accessibility and explainability standards Edinburgh's public-sector procurement expects, with human oversight built into every automated decision.
Senior-led delivery and a no-subcontracting model that gives Edinburgh clients direct access to the people actually building their AI system.
Awards & Recognitions
Recognised by leading platforms, startup ecosystems, and global technology communities.
Every Model Stays In-House, Start to Finish
Every AI engagement here runs entirely under our own roof. Whoever's building your AI system is who you're actually talking to, no account-manager relay in between.
AI Built to Earn Enterprise Trust
AI is part of the actual product from the first sprint here, not layered on afterward, with every feature judged on real production value rather than how it looks in a pitch.
One Senior Engineer Owns the Whole Build
A senior engineer leads every Edinburgh project, directly involved in architecture decisions, model reviews, and deployment, writing code alongside the team rather than managing tickets from a distance.
Trust-Grade Compliance From the First Sprint
UK GDPR, the Data Protection Act 2018, and any relevant sector regulator's expectations all get built into an Edinburgh AI system from the first design sprint, rather than left as a last-minute scramble before launch.
Talk Directly with Our Founder
Discuss your software vision, AI roadmap, and delivery strategy with the team leading product engineering at Akoode.
Ways of Working That Fit How Edinburgh Teams Actually Operate
Regardless of which model you choose, dedicated engineers, complete IP ownership, and direct access to the team building your AI system come as standard.
Dedicated Team
Most Popular
Best for: Projects requiring continuous development and long-term product evolution
A dedicated team works as an extension of your in-house engineers, designers, QA, and PMs — fully aligned with your roadmap and sprint cadence.
Full control over the development process and sprint priorities
Easy scalability as the product and team requirements evolve
Seamless integration with your existing tools and workflows
Direct access to engineers — no account managers in between
Straight answers on process, pricing, timelines, compliance, and what working with Akoode actually looks like for Edinburgh AI projects.
UK AI pricing for 2026 splits into three tiers worth knowing. API integrations run £8,000 to £15,000. Custom models trained on your own data typically run £15,000 to £75,000. Enterprise or fine-tuned work starts from £75,000. None of these are guesses, they reflect what UK businesses are genuinely paying, and a discovery call turns the range into a real number.
There's no default answer here, genuinely. Off-the-shelf APIs cover most generative use cases well and move fast. A custom model makes sense once a general-purpose model can't handle your specific data or task. Discovery decides this properly.
Both ends of the spectrum. Early-stage teams testing a first AI feature, growth-stage companies scaling what's working, and enterprise programmes running full transformations are all represented, with the engagement adjusted to fit.
Our teams operate dedicated India-UK overlap hours structured around GMT and BST, so Edinburgh clients get real-time collaboration during planning, model reviews, and deployment through Slack, Jira, GitHub, and weekly sprint reporting.
The real distinction is size and shape. A dedicated team is a whole self-contained unit working your project. Staff augmentation is narrower, placing a specialist or two inside a team you already have, addressing a specific hole rather than standing up a new structure.
Send your project through the contact form on this page and it gets read personally by a senior team member, with a reply within a business day. Complex data situations usually warrant a short paid discovery phase upfront.
Compliance work happens at the architecture stage, well before launch, not as a scramble once something's gone wrong. Anywhere personal data is involved, UK GDPR and the Data Protection Act 2018 get mapped directly into the model design, including a clear answer for how a given decision could be explained if a client is ever asked to justify one.
As a baseline, that's monitoring, drift detection, and infrastructure maintenance as underlying models change. Scheduled retraining and cost or latency tuning tend to get added once the system is genuinely handling production volume.
The honest answer hinges on your data, not your ambition. Expect four to six weeks for a straightforward API integration. A custom model built on your own data runs eight to fourteen weeks, and that stretches out if the data needs proper cleanup first.
We do both regularly, though integration into an existing product or workflow is more common than starting standalone. Whichever it is, the AI work is designed around your existing systems, not tacked on afterward.
It's genuinely the norm, not the exception. Discovery exists to catch this early. If your data needs meaningful cleanup before training, that gets scoped as its own piece of work rather than glossed over.
This happens more often than you'd think. First comes a technical audit of whatever already exists, the model, the pipeline, the codebase, then an honest quality and risk assessment, and only then a remediation plan before development actually resumes.
Always, and well before any genuine specifics are on the table. Everything a finished engagement produces, models, code, documentation, is yours outright once it's done; we retain nothing.
Not a dedicated one, by deliberate government choice. The UK set out a pro-innovation, principles-based approach in its March 2023 White Paper, deciding against a single horizontal AI law like the EU's. Instead, existing regulators, the ICO for data protection, the FCA for financial services, the MHRA for medical AI, the CMA for foundation models, apply five cross-cutting principles within their own sectors. The Data (Use and Access) Act 2025 also came into force in large part from February 2026, updating the rules around automated decision-making while keeping the underlying safeguards. We build to that real framework, not a horizontal AI Act that doesn't exist here.
Yes, and we'd push back on skipping it. Real-world data drifts away from what a model originally learned on, slowly enough that it's easy to miss until performance has already dropped. Monitoring, drift detection, and a retraining schedule are included as standard, either on retainer or as-needed.
Our strongest sector experience in the Edinburgh AI market covers Finance & Banking, Energy & Utilities, Public Sector & Government, with data-handling and explainability standards built to what those industries actually require.
Reading for Edinburgh AI Product Teams
Practical guidance on AI strategy, model deployment, and technical decisions for founders and product leaders building with AI.
Tell us about the project and we'll respond with a scoped estimate and a recommended way forward.
Reply Time
< 30 working minutes
NDA-Friendly
Signed before kickoff
IP Ownership
100% yours from day one
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