AI integration and automation: put AI to work in your business

AI integration and automation: LLM-powered features, chatbots, database intelligence and automated workflows, built with the right model for every job.

From clever demo to dependable system.

Anyone can wire a chatbot to an API in an afternoon. Making AI accurate, safe, affordable and genuinely useful inside a real business is the hard part, and it is the part we do. We bring AI into your backend, your database, your customer-facing product and your daily workflows, then tune it until it earns its place.

We are model-agnostic by design. We work with Claude, OpenAI, Gemini, Mistral, Grok, Llama and more, choosing per task for the best balance of quality, speed and cost. No loyalty to a vendor, only to the result.

Included in every AI build

Use case and model selection based on your goals and budget
Prompts that are engineered, versioned and tested, never guessed
Answers grounded in your own data, with sources where useful
Structured outputs your systems can rely on
Guardrails against made-up answers, prompt injection and data leaks
Human approval steps wherever the stakes are high
Usage tracking and spend caps, so the bill never surprises you
Fallbacks and monitoring, so a model outage is not your outage
Documentation and a prompt library handed over to your team

Every way AI can earn its keep in your business

From a single smart feature to a fleet of agents working in the background, these are the use cases we build most.

AI in your backend: Add intelligence behind the scenes: classification, data extraction from documents, summarisation, scoring and decisions, exposed as dependable endpoints.
Chatbots and assistants: Customer-facing or internal assistants that answer from your own content, like the course assistant we built for a learning platform, with cited sources.
Intelligence over your database: Ask questions of your data in plain English. AI deployed over your database surfaces insights, trends and anomalies without anyone writing a query.
Automation flows: AI that does routine work on your behalf, on a schedule or a trigger: triaging emails, updating records, drafting replies, producing reports.
Multi-agent systems: Several specialised agents collaborating on complex jobs, like the pipeline we built that turns meeting transcripts into an explorable knowledge graph.
Content and quality enhancement: Small touches with big payoff: polish copy, fix tone, translate, tag, summarise and generate descriptions inside the tools you already use.

What changes when AI is done properly

The goal is never "we use AI". The goal is hours back, better decisions and happier customers.

Hours back, every week: Triage, drafting, data entry and reporting handled automatically, so your team spends its time on work that needs a human.
Answers from your own data: Instead of generic replies, AI answers using your documents, records and policies, so what it says is actually relevant.
Decisions with insight: Patterns hiding in your data come to the surface, so you decide with evidence instead of instinct alone.
Always-on help: Customers and teammates get instant, accurate answers at any hour, without adding headcount.
Consistency at scale: The thousandth task is handled as carefully as the first, in your tone and by your rules.
A real edge: While competitors demo chatbots, you are running AI that saves money and wins customers.

Prompt engineering is engineering.

The difference between an AI feature people love and one they abandon is rarely the model. It is the prompting, the configuration and the fit to the use case. That is where we spend our time.

Right model for the right job: Deep reasoning task? A flagship model. High-volume classification? A small, fast, cheap one. We match model to task, and often use several in one system.
Prompts treated like code: System prompts, examples and output formats are written deliberately, versioned, and tested against real cases before they touch production.
Configured, not left on defaults: Temperature, context, tool use, structured outputs, caching: the settings that decide both quality and cost, tuned for your use case.
Grounded in facts: Retrieval, database queries and tool calls make answers come from your data, not from the model’s imagination.
Guardrails that hold: Defences against made-up answers, prompt injection and data leaks, plus human checkpoints wherever the stakes are high.
Cost under control: Model routing, caching, token budgets and spend alerts keep costs predictable as usage grows.

From "could AI help?" to running in production

Fourteen steps that take an AI idea from a promising guess to a monitored, cost-controlled system your team trusts.

Understand the goal and the workflow (Discover): What are you trying to improve, and how is it done today? We map the real process before suggesting any technology.
Audit your data and constraints (Discover): What data exists, how clean it is, who may see it and what rules apply. This decides what is possible and what is safe.
Pick the use case and define success (Discover): One focused starting point with a measurable definition of good: accuracy, time saved or cost per task. No vague "make it smarter".
Choose the model and architecture (Design): Which model for which task, hosted API or open model, single call or agent or workflow. Chosen for quality, speed, privacy and cost.
Engineer the prompts (Design): System prompts, examples, output schemas and guardrails, written deliberately and versioned like the code they are.
Prototype on real examples (Design): A working spike on your real data, so you see how it behaves before committing to the full build.
Build the integration (Build): Backend endpoints, chatbot interface, database layer or workflow, with streaming, retries, fallbacks and securely handled API keys.
Ground it in your data (Build): Retrieval, database access and tool calls, so answers come from your facts and every claim can point to a source.
Connect and automate (Build): Triggers, schedules and your existing apps wired in through tools like n8n, with approval steps wherever a human should decide.
Evaluate against real cases (Verify): A test set built from your real examples measures accuracy, catches made-up answers and compares models head to head.
Safety, privacy and cost review (Verify): Prompt injection, sensitive data handling, rate limits and budget caps checked deliberately, not assumed.
Pilot with real users (Verify): A small group uses it for real work, with humans reviewing the high-stakes outputs, until it feels trustworthy.
Deploy with monitoring (Launch): Logging, cost dashboards and alerts switched on, with a fallback path and an easy way to swap models as better ones arrive.
Hand over and keep improving (Launch): Documentation, a prompt library and a playbook for your team, then a review once real usage shows what to tune.

Every major model. No favourites.

The best model depends on the task, the budget and how sensitive your data is. We are comfortable across all of these, and we mix them when it makes the system better or cheaper.

Anthropic Claude

Careful, strong at long documents, analysis and agent work.

Claude Opus 5.5: Deepest reasoning for complex analysis and agents.
Claude Sonnet 5.5: The all-rounder: strong quality at production-friendly speed and cost.
Claude Haiku 4.5: Fast and economical for high-volume tasks.

OpenAI

Broad ecosystem, strong tool use and structured outputs.

GPT-5: Flagship for complex reasoning, coding and agentic tasks.
GPT-5 mini: Balanced quality and cost for everyday workloads.
GPT-5 nano: Smallest and cheapest, for simple high-volume jobs.
GPT-4.1: Dependable instruction following with a very long context.

Google Gemini

Huge context windows, multimodal, and a generous free tier to prototype on.

Gemini 3 Pro: Most capable Gemini for complex reasoning and multimodal work.
Gemini 2.5 Pro: Strong reasoning across very long documents.
Gemini 2.5 Flash: Fast, low-cost workhorse with strong quality.
Gemini 2.5 Flash-Lite: Cheapest and fastest for bulk processing.

Mistral

Efficient European models with excellent value and multilingual strength.

Mistral Large: Flagship for complex and multilingual tasks.
Mistral Medium: Strong quality at a friendlier price.
Mistral Small: Light and quick for simple tasks.
Codestral: Specialised for code generation and completion.
Magistral: Reasoning-focused for step-by-step problems.

xAI Grok

Fast, capable models with large context and real-time awareness.

Grok 4: xAI flagship for hard reasoning problems.
Grok 4 Fast: Cost-efficient with a very large context window.
Grok 3 mini: Compact reasoning at low cost.

Meta Llama and open models

Open-weight models you can self-host or run on Groq, ideal when data must stay in your hands.

Llama 4 Maverick: Meta’s multimodal open-weight flagship.
Llama 4 Scout: Very long context in a lighter model.
Llama 3.3 70B: Proven open workhorse, easy to host.
DeepSeek R1: Open reasoning model at low cost.
Qwen3: Strong multilingual open models in many sizes.

Model lineups change fast. We keep up with new releases and re-test, so you get the best current option, not last year’s.

Direct access to the best AI. Plumbing that stays out of the way.

We build on official APIs and proven platforms, with the glue and data layers that make AI dependable in a real business.

AI platforms and APIs

Google AI Studio: The quickest route to the Gemini API, with a generous free tier for prototyping.
OpenAI API: GPT models, plus embeddings, speech and vision when a project needs them.
Anthropic API: Claude models, directly or through cloud providers when you need enterprise controls.
Mistral API: Efficient models with strong multilingual performance and good value.
xAI API: Grok models for large-context and reasoning-heavy tasks.
Groq: Ultra-fast inference for open models, our pick when speed is the priority.

Native and open models

Llama: Self-hosted or on Groq when data must stay in your hands.
Ollama: Run open models locally for private development with zero API spend.
Hugging Face: The hub for open models, datasets and specialist fine-tunes.

Automation and integration

n8n: Visual workflows with schedules, approvals and hundreds of integrations. Self-hostable, so running costs stay low.
Make: Hosted no-code automation for teams who prefer to manage flows themselves.
Zapier: Quick wins when your apps are already supported out of the box.
Google Workspace: Gmail, Sheets, Docs and Drive as triggers and destinations for AI work.

Data and backend

Supabase: Postgres with vector search, sign-in and storage: a natural home for AI-ready data.
PostgreSQL: Reliable relational core, with vector extensions for retrieval.
Node.js: Streaming responses, tool calls and background jobs.
Vercel: Fast deployment for AI apps, with streaming and easy rollbacks.

Lean for you. Lean for us.

AI costs can quietly balloon. We design for the opposite: the smallest model that does the job well, caching and routing between models, spend alerts and free tiers while prototyping. You pay for intelligence where it matters, and not where it does not.

What working with us feels like

Lean on both sides: We pick right-sized models and put cost controls in from day one. If a simple rule beats AI for your problem, we will say so.
Model-agnostic: No vendor loyalty. We choose, and re-choose, the best model for each task as the market moves.
Engineered, not improvised: Prompts are written, versioned and tested against real cases. Quality is measured, not assumed.
Safe by design: Guardrails, privacy-minded data handling and human approval steps are part of the build, not an afterthought.
Built by people who ship: We have put multi-agent platforms and AI assistants into real production, and we bring those lessons to yours.

Frequently asked questions

What can AI actually do for my business?

Most valuable uses fall into a few groups: answering questions from your own content, extracting and organising information from documents, automating repetitive workflows, surfacing insights from your data and polishing content. In the discovery step we find which of these would pay off for you, and which would not.

Which AI model will you use?

The one that fits the task. We work with Claude, OpenAI, Gemini, Mistral, Grok and open models like Llama, choosing for quality, speed, cost and data sensitivity. Many systems use two or three models, each doing what it is best at.

Is my data safe? Will it be used to train AI models?

The major providers generally exclude data sent through their business APIs from model training by default, but terms differ by provider and plan, so we verify the current terms for each project. We also send only what is needed, mask sensitive fields and can use self-hosted open models when data must not leave your control.

How do you stop AI from making things up?

We ground answers in your own data, require sources where it matters, constrain outputs to defined formats, test against real cases and add human approval for high-stakes actions. No system is perfect, which is why we measure accuracy instead of assuming it.

How much will it cost to run?

AI usage is billed by volume, so costs scale with use. We pick the smallest capable model, cache repeated work, route simple tasks to cheaper models and set spend caps and alerts. You get a running-cost estimate before we build.

Can it connect to the tools we already use?

Yes. We connect AI to Google Workspace, email, CRMs, databases and most apps with an API, using custom code or tools like n8n, with approval steps wherever a person should have the final say.

How long does an AI project take?

Small enhancements can be live in days. Our AI-powered platforms, including a course assistant and a multi-agent knowledge graph, took between 14 and 25 days from kickoff to launch. We scope a focused first version, then improve it with real usage.

Do we even need AI for this?

Sometimes not. A simple rule, script or better process can beat AI on cost and reliability. If that is the case for your problem, we will tell you, and save you the money.