Senior engineers · since 2023
AI development
AI chatbots, AI agents, RAG/assistant features and LLM API integration on the surfaces customers actually use. OpenAI, Claude and DeepSeek on the model side, FastAPI on the server, retrieval over your own data, voice agents with Whisper, all shipped as real working products, not GPT wrappers.
01 — What we build
Built for production, end to end.
AI chatbots & assistants
Customer-facing chatbots and internal assistants grounded in your own data, so answers come from your documentation rather than the model's guesses. Each one ships with guardrails and observability from the first release.
RAG over your data
We chunk and embed your documents into a vector store, then tune and evaluate retrieval rather than assume it works. The index is versioned alongside the code so answer quality stays reproducible.
Agents & tool-calling
Agents that plan and call real tools against your calendar, CRM and internal APIs, with strict input and output validation at every boundary. The model decides what to do; deterministic code does the acting.
Voice & multi-modal
Voice agents built on the transcribe, reason, act loop: Whisper for transcription, an LLM for reasoning, and a deterministic tool layer behind it. A single service runs the whole flow with predictable latency and an audit trail for every call.
LLM integration into existing apps
Most AI work is wiring features into a product that already has users, not building from scratch. We add LLM capabilities to your current stack with documented prompt and cost engineering so spend stays predictable.
Guardrails & observability
Prompt-injection guardrails sit at the boundary and observability ships with the first release, so a drop in answer quality is caught before users notice. Cost monitoring and retrieval evaluation run from day one.
AI development at the studio means shipping product features customers actually use, not GPT-wrapped demos. Most engagements wire LLM features into a product that already has users: a chatbot grounded in the team's documentation, a voice agent that does tool-calling against a real calendar or CRM, or an internal operator-assist surface that turns a 30-minute lookup into a one-line query.
The pattern matters more than the model
We run the same engineering rigour on AI as on any production system. Prompt and cost engineering are documented from day one, RAG indexes are versioned alongside the code, prompt-injection guardrails are wired in at the boundary, and observability ships with the first release so a regression in answer quality is caught before the user notices it. OpenAI, Claude and DeepSeek are the default model surfaces because they ship fast and swap cleanly when a cheaper or better model catches up, the architecture doesn't marry one vendor.
Retrieval and agents
Most useful AI features need the model to know things it was never trained on. We build retrieval-augmented generation properly: documents chunked and embedded into a vector store (pgvector on PostgreSQL for most builds, a managed index where scale demands it), retrieval tuned and evaluated rather than assumed, and the index versioned alongside the code so answer quality is reproducible. Agents go a step further, the model plans and calls tools, and we wire those tools (often through a framework like LangChain) against real APIs with strict input and output validation, so an agent can act without going off the rails.
Voice and multi-modal
For voice and multi-modal, the standard architecture is Whisper for transcription, an LLM for reasoning, and a deterministic tool layer that talks to the real systems behind the conversation, the calendar, the CRM, the inventory database, the support inbox. A single Python service runs the transcribe, reason, act loop with predictable latency and an audit trail for every tool call. The same pattern moves into client work without rewriting it.
Automation and data pipelines
A lot of AI value is unglamorous plumbing: getting data in and out reliably. We build AI-assisted import and sync pipelines, and use workflow tooling like n8n to connect the systems a business already runs. For Power Vape Shop that meant importing products from a warehouse system and a CRM into Shopify with AI-assisted matching, then keeping stock in sync across physical shops and online. The model does the fuzzy matching; deterministic code does the moving.
When AI isn't the answer
The fastest win on most AI projects is figuring out which 20% of the brief actually needs an LLM and which 80% is better solved by a deterministic rule. We name that boundary in scoping and write the deterministic parts as plain code; the LLM gets the bits where ambiguity is the feature, not the bug.
02 — The right stack
Stack-agnostic, by design.
We're stack-agnostic by design. We pick the right tool for the problem in front of us, then ship it to production properly, not whatever we happened to use last time.
How we select: We weigh the problem, your team's skills, the hiring market and the long-term maintenance cost. The goal is software you can run and grow without us.
BACKEND
Python
Python is the native language of the AI ecosystem, with first-class SDKs for every model vendor and the retrieval and embedding tooling we rely on. The transcribe, reason, act loop for voice agents runs as a single predictable Python service.
Best for: LLM orchestration, RAG pipelines, voice and data-sync services
BACKEND
FastAPI
FastAPI gives us async request handling and typed request and response models, which matters when an agent is calling tools and streaming tokens back. Validation at the boundary keeps tool inputs and outputs strict so an agent can act without going off the rails.
Best for: Model-serving endpoints, agent tool layers, streaming responses
DATABASE
PostgreSQL
PostgreSQL with pgvector is our default vector store for retrieval, so embeddings live in the same database as the rest of the app data. That keeps RAG indexes versioned alongside the code and avoids a second managed service until scale genuinely demands one.
Best for: Vector search, embedding storage, application data
FRONTEND
Next.js
Next.js builds the surfaces customers actually touch the AI through, from chat UIs to internal operator-assist screens. Streaming responses and server routes let us put the model behind a clean, fast interface without a separate frontend stack.
Best for: Chat interfaces, operator-assist surfaces, streaming UIs
FRONTEND
TypeScript
TypeScript types the contract between the chat UI and the model service, so tool schemas and response shapes are checked at compile time. That cuts the class of runtime errors that surface when an LLM returns something unexpected.
Best for: Typed AI surfaces, tool schemas, end-to-end type safety
INFRASTRUCTURE
Vercel
Vercel hosts the Next.js AI surfaces with streaming support and edge delivery close to users. Preview deployments let us evaluate prompt and retrieval changes on a live URL before they reach production.
Best for: Hosting AI surfaces, streaming delivery, preview evaluation
03 — Why teams choose us
Senior engineering, clear accountability.
Fixed quote in 48 hours
We scope the work in writing within 48 hours, with price and timeline fixed before anything starts. No open-ended hourly drift.
Direct senior access
You work with the senior engineer who scopes and builds your project — no middle layer.
You own 100% of the code
Code, documentation and infrastructure are handed over at the end. No lock-in, no proprietary black boxes, nothing held hostage.
Reply within 4 hours
In UK business hours you hear back within four hours, and async over Loom for everything else. You always know where things stand.
AI where it earns its place
We use AI to move faster where it helps. A senior engineer decides what actually ships.
Rescue welcome
About a third of our work is inherited projects. We audit first, then fix or rebuild with the trade-offs spelled out plainly.
04 — The process
Agile, collaborative, and completely transparent.
Discovery & scoping
A short, focused kickoff. We learn the problem, agree the scope, and put price and timeline in writing within 48 hours.
UX/UI design
Wireframes then visual design, reviewed with you before a line of production code is written. No surprises later.
Development
Senior engineers build in weekly increments. You get a live preview and a Loom walkthrough every Friday.
QA & testing
We test as we go and again before launch, across devices and the edge cases that actually break things.
Deployment
We ship to production carefully, with monitoring in place and a rollback ready if anything misbehaves.
Post-launch
Code, docs and a handover walkthrough. Then support or a maintenance retainer if you want us close by.
05 — Selected work
Recent work in this space.
What clients say
“Devonic Web combines deep technical knowledge with strong communication. They listen carefully, understand your needs, and bring their expertise to the table with clarity and confidence.”
06 — FAQ
Common questions.
Can you add AI to our existing product?
Yes. Most AI work is wiring LLM features into a product that already has users, not building a chatbot from scratch.
How do you stop the AI from hallucinating or going off-script?
Retrieval grounds answers in your own data, guardrails sit at the boundary, and observability ships with the first release so answer-quality regressions are caught early.
Which models do you use?
OpenAI, Claude and DeepSeek. The architecture isn't married to one vendor, so we swap as cheaper or better models arrive.
Do you build voice agents?
Yes. Whisper transcription, LLM reasoning and a deterministic tool layer against your calendar, CRM and internal APIs, shipped end to end.
RELATED SERVICES
07, START A PROJECT
Got a project in mind?
Tell us what you're building. We reply within 4 hours during UK business hours.