AI integration services
Add AI to the software you already have
You do not need a new platform to use AI well. We integrate LLM features into your existing product and systems — summaries, extraction, smart search, generated reports, in-app assistants — inside your codebase, on your infrastructure, with the testing and monitoring your other features already have.
The hard part is not calling the API
Calling an LLM is one line of code. Shipping an AI feature your customers rely on is not: outputs have to fit your data model, latency has to suit your UI, costs have to be predictable at your usage, failures need graceful fallbacks, and you need to know when a prompt change makes things worse.
We are full-stack engineers first, so we integrate AI the way we would build any feature — in your stack, through your review process, with tests — rather than bolting on a separate AI service nobody on your team understands.
AI features we commonly add
AI document processing and extraction
Turning emails, PDFs, forms and free text into validated records in your database, using schema-constrained output so the result always fits.
Generated content and reports
Summaries, executive reports, product descriptions or drafts generated from your data — with templates and rules that keep them consistent.
Semantic search
Search that understands meaning across your product's records and documents, combined with the filters users already rely on.
In-product assistants
An assistant that answers questions about the user's own data and can take actions in your app with their permissions.
Classification and routing
Categorising tickets, leads or transactions and routing them to the right queue or person, with accuracy measured against your labels.
Connecting AI tools to your platform
MCP servers and APIs that let Claude, ChatGPT and your team's AI tools work with your system directly.
What an integration includes
Every AI feature we ship comes with the same engineering scaffolding:
A provider-agnostic model layer
Models behind one interface, so you can switch or mix providers without touching feature code.
Evaluation suite
Test cases drawn from your real data, run in CI on every prompt or model change.
Fallbacks and limits
Timeouts, retries, rate-limit handling and a sensible non-AI fallback when the model is unavailable.
Cost and usage tracking
Per-feature and per-customer token tracking, so pricing and margins are based on numbers.
Privacy controls
Redaction of fields the model does not need, and enterprise or zero-retention endpoints where your contracts require them.
Observability
Traces of inputs, outputs and latency, so support can see what the AI did for a specific customer.
Integrations we have shipped
AI-generated assessments and executive reports for a leadership consultancy
A team-assessment app where admins generate assessment templates from a topic with an LLM, participants answer through share links, and the system writes executive-summary reports.
Read the case studyAI integration · MCPConnecting an operations platform to Claude with a remote MCP server
A remote Model Context Protocol server that lets the consultancy's staff run their admin platform by asking Claude — invoicing, scheduling, follow-ups, time entries and contracts — with the same permissions they have in the app.
Read the case studyEngineering choices we make by default
Structured output over free text
If software consumes the result, we use schema-constrained output and validate it. "Usually valid JSON" is not good enough for a production feature.
Give the model less
Models get the minimum context they need — labelled findings rather than raw numbers, relevant records rather than whole tables. It improves accuracy and lowers cost.
Streaming where users wait
Streaming responses for interactive features; background jobs and queues for everything else, so AI latency never blocks a request.
Ship behind a flag
New AI features roll out to a subset of users first, with feedback captured, before going to everyone.
How we work with your team
- 1
Pick the feature with the clearest value
We review your product and data and recommend where AI will measurably help — and where it will not.
- 2
Prove it on your data
A short spike against real examples, with an evaluation score, before any UI work.
- 3
Build it into your codebase
We work in your repository and follow your conventions, code review and deployment process.
- 4
Launch gradually and hand over
Feature-flagged rollout, monitoring, documentation and a walkthrough so your team can own it — or we stay on to iterate.
Integration and deployment considerations
Your stack, not ours
We work in React, Next.js, Node.js, Python/FastAPI, mobile and Electron codebases. AI code lives next to the feature it powers.
Multi-tenant SaaS
Tenant isolation, per-tenant limits and usage metering are designed in from the start, not retrofitted.
Compliance
We document what data reaches which provider so your security questionnaires and DPAs stay accurate.
Pricing your AI feature
Usage data from the pilot informs whether the feature is included, metered or sold as an add-on.
Technology we use
LLM APIs
- Anthropic Claude
- OpenAI
- Google Gemini / Vertex AI
- Groq
SDKs & frameworks
- Vercel AI SDK
- LangChain
- LangGraph
- Model Context Protocol
Application stack
- React
- Next.js
- Node.js
- Python / FastAPI
- React Native
- Electron
Data & ops
- PostgreSQL / pgvector
- Redis
- Langfuse
- Sentry
- OpenTelemetry
Who this is for
- SaaS founders whose customers are asking for AI features.
- Product teams without in-house LLM experience who want it done properly the first time.
- Businesses with internal systems full of documents and records that people process by hand.
- Teams that tried an AI feature and found it unreliable, slow or too expensive.
What drives the cost
A single well-scoped AI feature in an existing codebase is usually measured in weeks, not months. We quote after reviewing your product and data, and we will tell you if an off-the-shelf tool would serve you better than custom work.
- State of your codebase and APIs
- How structured and accessible the relevant data is
- Accuracy requirements and evaluation effort
- UI complexity (streaming, editing, review flows)
- Multi-tenancy, privacy and compliance requirements
- Expected usage volume and model cost targets
Further reading from our engineers
- Building a production MCP server for your SaaS: auth, tool design and lessons from ~115 tools
What it actually takes to put a Model Context Protocol server in front of a real product: remote transport, OAuth, permission scoping, tool design and testing with real clients.
- How much does it cost to build an AI agent? A breakdown from real builds
The two bills behind every AI agent — building it and running it — with the drivers that move each, and worked token-cost arithmetic at current API prices.
- From AI proof of concept to production: what demos skip
The gap between an AI demo and a feature you can ship: evaluation sets, judges you can trust, structured output, permissions, cost tracking, fallbacks and a gradual rollout.
- RAG vs fine-tuning for business knowledge: a decision framework
When to retrieve, when to fine-tune, when to do both — and the retrieval failures that actually decide whether your AI answers are right.
- How to add AI features to an existing SaaS without a rewrite
The engineering playbook we use to ship LLM features inside existing products: what to build first, the architecture defaults, tenancy, privacy and rollout.
Need an engineer inside your own team instead of a project? Hire AI developers from DablewDevs on an hourly or dedicated basis.
Related services
AI Integration Services: common questions
Tell us about your product and the feature you have in mind
Tell us what you are building or what needs fixing. You will hear back from an engineer — not a sales rep — within one business day, usually with questions or a rough approach.
Your enquiry goes straight to our engineering team — the people who will scope and build your project, not a sales desk.