AI Integration Services
AI capabilities inside the product you already have, behind a clean boundary, without a rewrite, without betting the roadmap on one vendor.
Who this is for
The situations that bring people to us
The board wants AI in the product. The codebase disagrees.
You have a working product and a real roadmap. What you need is AI added surgically, not a rebuild disguised as an integration.
Which provider? Which model? It changes monthly.
Model rankings shuffle every quarter. The durable decision is an abstraction boundary that makes switching providers a config change, not a migration.
The demo cost pennies. Production has a bill.
Token costs, latency, and rate limits behave very differently at real traffic. Caching, routing, and batching are where integrations succeed or quietly bleed.
What we build
Concrete deliverables, not categories
LLM features in existing apps
Summarization, drafting, extraction, matching, and assistants built into your current codebase and UX, shipped as increments, not a rewrite.
▸ In production: the RocketJob resume-tailoring engine, built for a client
The AI boundary layer
A contract between your product and the model world: provider abstraction, structured outputs, fallbacks, and versioned prompts your team can maintain.
Cost & latency engineering
Caching, model routing by task, streaming UX, and usage observability, so the feature is fast for users and sane on the invoice.
Automation integrations
AI wired into your operational tools (CRM, helpdesk, sheets, WhatsApp) through n8n and direct API work.
How it works
Integration without entanglement
The pattern that keeps integrations healthy is a hard boundary: your product calls a layer you own (typed contracts, structured outputs, retries, evals) and that layer talks to models. Retrieval sits inside the boundary so features answer from your data; provider routing sits inside it so the model of the month is a configuration decision.
Everything on your side of the boundary stays testable, deterministic software. Everything on the far side is replaceable. That is what lets you ship AI features this quarter without inheriting a strategic dependency you did not choose.
Proof, not promises
RocketJob: AI integrated where it earns its keep
A client's job-search platform: a resume-tailoring engine with versioned AI rewriting and pixel-faithful PDF output, integrated into an existing product flow rather than bolted on beside it.
How we work
Four steps, each with an artifact
Artifacts beat adjectives. Every step of an engagement ends in something you can hold us to.
Step 1
Scoping call
→ Written scope & estimate
Thirty minutes on what you are building and why. You leave with a written scope, an honest estimate, and our view on whether AI is even the right tool.
Step 2
Architecture sprint
→ System design document
We design the system before we bill for building it: data flows, model choices, failure modes, and the success measure we will be judged against.
Step 3
Build in weekly demos
→ Working software, week one
Short cycles, working software every week, and decisions made in the open. You see progress in the product, not in status reports.
Step 4
Launch & run
→ Monitoring, evals & handover
We ship it, instrument it, and either run it with you or hand it over with documentation your team can actually operate from.
Integration work ships as increments: first the boundary layer and one feature end to end, then the next features ride the same rails. Your team reviews every increment, and the boundary layer is documented as yours from day one.
Stack for this work
Straight answers
Questions buyers actually ask
Will this disrupt our current product or users?
It should not. That is the point of the boundary-layer pattern. AI features ship as additive increments behind flags, your existing flows keep working untouched, and anything that underperforms can be rolled back at the flag, not the codebase.
Which AI provider should we use?
We are deliberately neutral: the honest answer changes by task and by quarter. We route by task (cheaper models where quality allows, stronger ones where it matters) behind an abstraction, so the provider question becomes routine configuration rather than architecture.
How do you keep API costs under control at scale?
Caching identical and near-identical calls, routing tasks to the cheapest model that clears the quality bar, batching where latency allows, and putting usage on a dashboard next to the feature metrics. Cost is treated as an engineering requirement from the first design, not a surprise on the second invoice.
Our stack is unusual. Can you still integrate?
Almost certainly. The boundary layer speaks HTTP and queues, which nearly every stack can reach; we have integrated through REST, webhooks, message queues, and direct database adapters. The scoping call is where we confirm the seams, and we will say plainly if something is a poor fit.
What does an AI integration cost?
A first feature shipped end to end (boundary layer included) typically lands in the low five figures (USD). Subsequent features are markedly cheaper because they reuse the rails. The drivers are integration surface area and reliability requirements, not model glamour.
Related AI development services
Engineering deep-dives on ai integration
Next step
Talk to us about your ai integration project.
Thirty minutes. You leave with a written scope and an honest opinion, including whether this is the right tool at all.
Prefer async? hello@visionnexera.com · We reply within one business day.