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AI Integration

AI features, added inside the product you already have.

You do not always need a new product. Sometimes you need an AI feature inside the site, app, or platform you already run: semantic search, generation, classification, summarization, recommendations, embeddings. We add it behind a clean interface, with the model as a swappable part, and we handle the parts people skip: cost and latency, evaluation, graceful fallbacks, and observability. The code is yours.

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INSIDE YOUR APP SWAPPABLE MODEL YOU OWN THE CODE
The overview

For teams who have the product and want AI in it.

This page is for a specific situation. You already have a working application: a site, a SaaS product, an internal platform, something your customers or your team use every day. It does its job. You just want to add an AI capability inside it, where the work happens, rather than send people off to a separate tool. That is AI integration: AI as a feature of the thing you already run, not a new thing standing next to it.

The features that fit this shape are narrower and more dependable than a general assistant. Semantic search that finds by meaning instead of exact words. Generation that drafts text where a user is stuck. Summarization that condenses a long document or thread. Classification that tags and routes things automatically. Recommendations that surface what is relevant. Embeddings, the numeric representations of text underneath several of these, that let your application reason about similarity. Each is a defined capability with a clear input and output, which is what makes it safe to add to a system that already works.

We add these behind a clean, testable interface, so the AI is one well-defined part of your system rather than a tangle threaded through everything. That boundary is the difference between a feature you can maintain and a science experiment bolted to the side. It lets the feature be tested in isolation, lets the model behind it be swapped without touching the rest of your app, and lets a failure in the AI layer degrade gracefully instead of taking the page down.

Features we add

Defined capabilities that drop into an application you already have.

  • Semantic search that finds by meaning
  • Generation that drafts and assists in place
  • Summarization of long documents and threads
  • Classification and automatic tagging
  • Recommendations surfaced where they help
  • Embeddings your application can reason over
What is included

The boundary, and everything that makes it hold.

Adding an AI feature is the easy part. Adding one that stays fast, predictable, and trustworthy in production is the work. These are the pieces we build into every integration, sized to what the feature actually needs rather than padded with machinery you will never switch on.

The first item is the one the others depend on. A clean interface around the AI layer is what makes the model swappable, the feature testable, and the failure mode graceful. The rest follows from getting that boundary right.

What's included

  • A clean, testable interface around the AI layer
  • A swappable model behind that boundary, no rewrite to change it
  • Cost and latency handled: caching, streaming, sensible limits
  • Graceful fallback when the AI layer is slow or unavailable
  • Evaluation on real examples, re-runnable as things change
  • Observability: tracing, token and cost accounting, error tracking
  • Grounding in your own content where the feature reads from it
  • Built to your stack, with documentation your team can extend
How we work

Fit it to the app, prove it, make it dependable.

We work inside your codebase and your conventions, not around them. The aim is a feature your developers could have written, with the AI-specific parts (the boundary, the evaluation, the fallbacks) done right so they do not have to learn them the hard way.

// a dependable component, not a tangle threaded through everything

  1. Understand the feature and the stackWe start with the specific capability you want and where it lives in your application, then read enough of your codebase to fit the work to your stack and conventions. Sometimes the honest answer is that an existing tool or a non-AI approach is better, and we will say so before writing anything.
  2. Design the boundaryWe define the clean interface the AI sits behind: what goes in, what comes out, and what happens on failure. This is what keeps the model swappable, the feature testable in isolation, and a bad day in the AI layer from spreading into the rest of your app.
  3. Build and ground the featureWe implement the capability against the boundary, grounding it in your own content where it reads from your data. For search that often means a hybrid of keyword and semantic matching with re-ranking on top, because exact terms and meaning each catch what the other misses.
  4. Handle cost, latency, and evaluationWe keep model calls off the critical path where we can, stream long responses, and cache repeated work so the feature stays responsive and affordable. We define what good looks like and test against real examples, so quality has a bar you can re-run as the model and your data change.
  5. Ship with observability, then hand overWe add tracing, cost and token accounting, and error and latency tracking so you can watch the feature in production rather than assume it is fine. The code ships in your repositories, documented, with the model swappable and the feature yours to maintain or extend.
Where this fits

Where it helps, and where it does not.

AI integration is the right call when you already have a product and a defined feature you want inside it. It is the wrong call dressed up as the right one when the feature is really a whole assistant, an end-to-end automation, or a new application, and we would rather name that than quietly build the wrong scope.

We are also candid about what the AI itself can and cannot do. Grounding a feature in your content reduces how often a model invents an answer, but it does not make it impossible, so anything consequential shows its sources or keeps a person in the loop. A confident wrong answer is worse than a visible "I am not sure." We design for that honestly rather than promising it away.

If a packaged product already does the job well, or if the feature would not earn the cost and latency it adds, we will tell you that too. The point is a capability that pays for itself inside your app, not AI added because it is fashionable.

A good fit, and a poor one

  • You have a working app and want a defined AI feature in it
  • Search, drafting, summarizing, tagging, or recommending
  • The capability reads from content you already hold
  • You want to own and maintain the result yourself
  • What you need is a full assistant or a new product instead
  • The task should be automated end to end, not added as a feature
  • A packaged tool already covers it well enough
  • The feature touches decisions that must stay fully human

// honest scoping, and a candid "not here" when that is the truth

Questions, answered plainly

Frequently asked questions

This is for teams who already have the product. You have a working site, app, or platform, and you want AI inside it: search that understands meaning, a draft button that helps a user who is stuck, summaries on long content, automatic tagging. We are not rebuilding your application or standing up a separate system next to it. We add a well-defined AI feature into the codebase you already run, behind a clean boundary, fit to your stack and your team's conventions. If what you actually need is a new assistant from scratch or a process automated end to end, those are neighboring services and we will point you to the right one.

We treat the model as a swappable part behind a clean interface rather than wiring your application directly to one vendor. That interface is the whole point: it means the model can be changed without rebuilding everything around it, so when a better or cheaper option appears, swapping it is a contained change rather than a rewrite. We choose what fits the feature by weighing capability, cost, latency, and where your data is processed, and we document the choice so you understand what is running and why. The landscape moves fast, and the design assumes it will keep moving.

It can, which is exactly why we design for it from the start rather than discovering the bill later. A model call is slower and costlier than ordinary application logic, so we keep it off the critical path where we can, stream long responses so the interface stays responsive, and cache results for repeated or near-identical requests so you are not paying to answer the same question twice. We also set sensible limits on how much work a single request can trigger. The goal is a feature whose cost and latency are predictable and watched, not a surprise that scales with your traffic.

Both will happen, so we build for both. Grounding a feature in your own content reduces how often a model invents an answer, but it does not eliminate it, so for anything consequential the feature shows its sources or keeps a person in the loop rather than presenting a guess as fact. And because the AI layer sits behind a clean boundary, a failure there degrades gracefully: search falls back to ordinary keyword results, a draft button quietly disables itself, the page keeps working. An AI feature should never be the thing that takes your application down.

We define what good looks like before we build, then test against it on real examples from your content rather than a tidy demo set. That gives us a bar we can re-run as the feature, your data, and the underlying model all change, so a regression shows up as a failed check instead of a user complaint. In production we add observability: tracing for individual requests, token and cost accounting, and latency and error tracking, so quality is something you can watch on a dashboard rather than assume.

Most existing search matches keywords: it finds documents that contain the words the user typed. Semantic search matches meaning, using embeddings, which are numeric representations of text where similar ideas sit close together. So a search for "cancel my plan" can surface a help article titled "ending your subscription" even with no shared words. In practice the strongest setups are hybrid, combining keyword and semantic matching and then re-ranking the top results for precision, because keyword search is still better at exact terms, names, and codes. We fit it to your content and tell you honestly whether it is worth it for your case.

You do, and yes. The feature lives in your repositories alongside the rest of your application, written to your stack and your conventions, with documentation aimed at your developers. There is no proprietary layer you have to keep paying us to touch. Because the AI sits behind a clean, testable interface, your team can maintain it, swap the model, or extend the feature without needing us in the loop. If you ever want to take it fully in-house, it is already yours to take.
The rest of the codex

Related services

AI integration is one of the ways we put AI to work. If what you need is a full assistant, a task automated end to end, or a plan before any building, these are the neighboring pieces.

THE HUB AI Systems & Automation Overview of AI Systems & Automation services
AI Assistants & Chatbots Assistants grounded in your own content, with citations and human handoff
Workflow & Document Automation AI on intake, routing, document extraction, classification, and reporting
AI Agents Agentic systems that take multi-step actions, with guardrails and human-in-the-loop
AI Strategy & Readiness Where AI pays off, data and tooling readiness, governance, and rollout

Have a product, and a feature you want AI to handle inside it?

Tell us the app and the capability you have in mind. We will give you a straight read on whether it fits as an integration, what the clean boundary around it looks like, and what it takes to keep it fast and dependable in production.

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