A model in a notebook is a demo. A model in production is an infrastructure problem.
We build the backend your AI features actually run on: gateways, retrieval, queues, caching, and the cost and rate controls that keep a clever prototype from becoming an unpredictable bill.
Serving path
A request comes in. Sometimes the cache answers instantly. Sometimes it takes retrieval, a queue, and the model. The infrastructure decides which.
The problem
The model is the easy part. The backend around it is the part nobody budgeted for.
Wiring up a model is genuinely easy now. A few lines, an API key, and you have something that works in a notebook and dazzles in a demo. That is the part everyone sees, and it is the part that is mostly solved. The reason AI products are hard is everything that has to happen around that one call before real users touch it.
In production the questions pile up fast. What happens when the model is slow, or down, or rate-limits you mid-request? What stops one buggy loop from making fifty thousand calls and a five-figure bill overnight? Where do the embeddings live, and how do you search them in milliseconds instead of seconds? How do you cache the answer you just paid for so you do not pay for it again? None of that is in the demo, and all of it is in the backend.
We build that backend. The gateway between your product and the model, the vector store and retrieval that feed it, the queues that absorb spikes and long-running calls, the cache that stops you paying twice, and the cost and rate controls that turn an unpredictable bill into a number you can plan around. The unglamorous layer that decides whether your AI feature is a product or just a very expensive demo.
Here is the version you will recognize. You shipped on Friday. The demo was clean: one call from your laptop, an answer in a second, everyone nodded. Monday it met real traffic. The first thing that broke was the spinner that never stopped, because the model under load takes four seconds, not one, and you were calling it inline on a request that users expected to be instant. Then the errors started: the provider began returning 429s because everyone hit the feature at once, and your code had no retry, so it just failed in front of people. Then finance forwarded the invoice, because a retry loop you wrote in a panic had quietly called the model forty thousand times overnight. Nothing in the demo predicted any of that, and all of it was waiting in the backend you had not built yet.
The thing nobody tells you
You are treating a model call like a function call. It is neither fast nor free.
The deepest bug in most AI features is not in the code. It is in your head. The model call looks like every other call in your program: a function name, some arguments, a return value. So you reach for it the way you reach for a function. You call it inline. You call it once per item in a loop. You call it on every render. That instinct is correct for a function and wrong for a model, and the gap between the two is where the latency, the bills, and the outages come from.
Start with speed. A database query that hits an index returns in single-digit milliseconds. A model call routinely takes hundreds of milliseconds to several seconds, because the model is generating the answer one token at a time, autoregressively, and the response cannot finish until the last token is written. That is not a slow query you can tune away. It is the floor. So the longer the output, the longer the wait, by design. Any place you would happily wait on a database read, a model call will feel broken.
Now the meter. A database read is effectively free; you can run a million of them and nobody notices. A model call is priced per token, every single time, with no free repeats. The cost does not show up in your editor or your tests. It shows up at the end of the month, all at once, as one number. So the loop that calls the model once per row in a thousand-row table is not a slow loop. It is a thousand paid calls, and you will only feel it when the invoice lands.
This is why the backend exists. Once you accept that the call is slow and metered, the rest follows on its own. Slow work that users wait on belongs behind a queue, not inline. An answer you already paid for belongs in a cache, not bought again. A call that can fail or rate-limit belongs behind a gateway that retries and falls back, not naked in your request handler. None of it is exotic. It is just what you build the moment you stop pretending the model is a function.
How we build it
Built for a model that is slow, fallible, and metered.
A backend for AI is not a normal backend with an API call bolted in. The model is slow, sometimes wrong, sometimes down, and it charges you per request. These are the patterns that account for all of that.
A gateway between you and the model
Your product does not call the model directly. It goes through a gateway that handles retries, timeouts, fallbacks to a cheaper or backup model, and the switch from one provider to another without rewriting your app. One place to control how every AI call behaves, instead of that logic copy-pasted across the codebase. This is where the provider's bad days get absorbed. When the API returns a 429 because you are sending too many requests, or a 529 because the provider itself is overloaded, the gateway does not just fail in front of a user. It waits and retries with exponential backoff and jitter, so a thousand clients do not all retry at the same instant and make the problem worse. After a couple of failed retries it routes the same request to a backup model or a second provider. The right place to choose a cheap model for an easy request and an expensive one only when the request actually needs it is here too, not scattered through your features.
Retrieval that is fast and actually relevant
Embeddings live in a vector store sized for your data, and retrieval is tuned so the model gets the right context in milliseconds, not a slow scan of everything. The quality of an AI answer is mostly the quality of what you fed it, so we treat retrieval as part of the infrastructure, not an afterthought bolted on at the end. Retrieval also has a quieter job: it keeps your context small. Every chunk you stuff into the prompt is tokens you pay for and tokens the model has to read before it can start answering, which is part of why the first token takes as long as it does. Fetching the five passages that matter, instead of the fifty that might, makes the answer both cheaper and faster at the same time. We tune what gets retrieved, how it is chunked, and how much of it actually reaches the model, because more context is not the same as better context.
Queues for the work that takes its time
Some AI calls are slow, some are huge, some are batch jobs over thousands of items. Those go on a queue and run out of band, with retries and progress, so a long generation never blocks a web request and a spike in demand becomes a slightly longer wait instead of a pile of timeouts and angry users. This is the part people try to solve by adding servers, and it does not work. The model has a fixed speed and the provider has a fixed rate limit, so ten more web servers just means ten more requests stuck waiting on the same slow, capped model. Spiky traffic against slow inference is a queueing problem, not a capacity problem. The queue lets bursts arrive faster than the model can serve them, holds the overflow, and drains it at whatever rate the provider actually allows. Users get a job that is accepted and tracked instead of a request that hangs until it times out.
Cache so you do not pay for the same answer twice
The same questions get asked over and over. We cache results where it is safe to, so a repeated request is served instantly and for free instead of going back to the model every single time. Caching is the single biggest lever on both latency and cost, and most products leave it sitting on the table. There are two caches worth running and they save you in different ways. The response cache stores whole answers, so an identical request never reaches the model at all. The prompt cache works one level down: most of your prompt is the same every call, the same instructions, the same system context, and without caching the model has to read and pay for that entire prefix from scratch every single time. Cache the prefix and you stop paying to reprocess the part that never changed. Both turn the most common requests, the ones that would otherwise dominate your bill, into the cheapest ones you serve.
Cost and rate controls, on by default
Every call is metered, budgeted, and rate-limited. A runaway loop hits a ceiling instead of your credit card. You can see what each feature costs, set limits per user or per tenant, and get an alert before a bill becomes a surprise. The controls are built in from the start, not discovered after the first scary invoice. The reason this matters more for AI than for anything else you run: with a normal backend, a bug usually costs you a crash, and a crash is loud and free. With a metered model, a bug costs you money, quietly, and keeps costing until someone notices. A retry loop with no ceiling, a user who scripts your endpoint, a prompt that accidentally grew ten times longer, none of those page you at 2am. They just bill you. So the ceiling is enforced in the path of every call, per feature and per user, and the system refuses the request that would blow the budget instead of trusting a human to be watching the dashboard at the moment it happens.
Streaming with backpressure, not a buffer that fills up
When you stream a model's answer token by token, the user sees words appear immediately instead of staring at a spinner for the full generation. That is the right experience, and it hides a trap. The model can produce tokens faster than a user on a phone on a train can receive them. If you just push everything out as it arrives, the unsent tokens pile up in your server's memory, one slow reader at a time, until a few hundred of them are quietly holding your process hostage. So we wire backpressure through the whole stream: when the client is slow to read, we slow the producer to match, instead of buffering the entire response in memory and hoping. A connection that drops mid-stream is detected and the work behind it is stopped, so you are not paying the model to keep generating an answer that nobody is listening to anymore.
"The model is a one-line call anyone can make. The backend is what stands between that one line and a 2am page, a runaway bill, and a product that falls over the first time it gets popular."
What you get
The infrastructure your AI runs on.
The layer between your product and the model, built for production: fast, observable, and impossible to accidentally bankrupt yourself with.
- A model gateway with retries, timeouts, fallbacks, and provider switching in one place
- Vector storage and retrieval tuned to return the right context in milliseconds
- Queues for slow, large, or batch AI work, so nothing blocks a user request
- A caching layer that serves repeated answers instantly and for free
- Cost metering, budgets, and rate limits per user or tenant, with alerts before a surprise bill
- Observability into every call: latency, cost, failures, and what the model actually returned
- The full repository and documentation, so your team can extend it without fear
Have an AI feature that works in the demo and worries you in production? Tell us what it calls and how often, and we will tell you what it will cost and where it will break.
Invoke usIs this the right call
When this fits.
Good fit
- You have an AI feature that works and now has to survive real traffic, real costs, and real failures
- Your AI calls are slow, unpredictable, or occasionally rack up a bill you did not see coming
- You need retrieval (vector search) running fast and reliably behind your AI features
- You are calling a model straight from your app and that logic is now scattered everywhere
Wrong call
- You are still figuring out whether the model works for your problem at all. Start with a Proof of Concept in Discovery.
- You want a one-off script that calls a model once. That does not need this infrastructure.
- You need the model itself built or fine-tuned, not the serving around it. That is our ML Engineering work.
Deployment and scale
Observable, budgeted, and ready for a spike.
Every AI call is observable: how long it took, what it cost, whether it failed, and what came back. When a feature gets slow or expensive, you can see exactly which calls are responsible instead of staring at a single rising number on the bill and guessing.
Cost is a first-class metric, not a quarterly surprise. Budgets and rate limits are enforced in real time, per feature and per user, so usage stays inside a number you chose. The system protects your spend automatically instead of relying on someone happening to watch a dashboard.
It is built for spiky, uneven AI load. Queues absorb bursts, caching flattens the repeated work, and the model calls scale independently of the rest of your product, so a sudden surge of AI usage does not take the whole backend down with it. Popularity becomes a cost question, not an outage.
What we settle before we begin: which models and providers you depend on, how much each AI feature is allowed to cost, and which calls have to be fast versus which can run on a queue. Everything else follows from those three.
Tell us what your AI calls, and how scared you are of the bill.
Describe the AI features, the models behind them, and where it gets nervous: the latency, the costs, the failures you have not handled yet. We will tell you what the backend should look like to make it fast, reliable, and affordable, and the shortest honest path there.