Your model is brilliant. Your users will judge the text box.
We build the front end of your AI product: the streaming, the visible tool calls, the recovery, the trust. The layer that decides whether people believe the answer or close the tab.
Streaming
Tokens land as they are written. The user reads while the model thinks. When it has to look something up, you see that too.
The problem
The model is the engine. The interface is the whole car the user sits in.
Teams pour months into the model: the prompts, the retrieval, the evals, the fine-tuning. Then they give the interface an afternoon and wonder why nobody trusts it. To your user, none of that backend exists. There is a text box, a cursor, and whatever happens after they hit enter. That is the entire product, as far as they will ever know.
And AI does not behave like normal software. It is slow in bursts, sometimes wrong, sometimes refuses, sometimes calls a tool and vanishes for eight seconds with no explanation. A normal interface treats all of that as an error to hide. A good AI interface treats it as the main event, because it is what happens most of the time.
This is a different job from building the model, and we keep the two separate on purpose. The brain, the agents and pipelines, is one practice (that is our AI Solutions work). The face, the interface a human actually touches and decides to trust, is this one. A brilliant model behind a confusing, silent, fragile interface is, to the person using it, just a confusing product.
Picture the demo that won everyone over. You type a question, the answer streams back, the room nods. Then a real user asks something at the edge of what the model knows. It pauses. The screen sits empty for nine seconds. They hit enter again, now there are two requests in flight, and the answer that lands sounds certain about a date it actually guessed. No source to check. No way to edit the question without retyping it. The user does not think "the retrieval missed." They think "this thing makes stuff up," they close the tab, and they tell a colleague it does not work. The model never changed. The interface decided the whole story. If that is the bug report you keep getting, you are in the right place.
The thing nobody designs for
Your AI will be confidently wrong. The interface decides if that is cheap or fatal.
Here is the truth most teams design around instead of for: a language model does not know when it is wrong. It produces the next likely token whether the answer is right, half-right, or invented, and it does all three in the same calm, fluent voice. There is no built-in tell. The model that nails a hard question and the model that fabricates a citation look identical on the way out. So the burden moves to the interface. Your UI is the only thing standing between a plausible guess and a user who acts on it.
That flips the goal. Most software is built to be right, so the interface optimizes the happy path and treats everything else as an exception to hide. An AI interface cannot win that way, because being wrong sometimes is not an exception here, it is the baseline behavior of the system. The job is not to make the model perfect. The job is to make noticing and recovering from a wrong answer almost free. A user who can spot the mistake in two seconds and fix it in one trusts the product. A user who only finds out after they have shipped the wrong number never comes back.
So the rule we hold to is simple: never render a guess as a fact. When the model is sure and the answer is grounded in something checkable, show it plainly. When it is reaching, the interface has to say so, and it does not need a percentage to do it. "Best match" next to "other options," a source link the user can open, a quiet "I am not certain, here is what I based this on" carries more honesty than a confidence score nobody calibrated. The cheapest trust signal you can build is letting the user see where the answer came from and decide for themselves.
And the unglamorous states are the actual product. The empty state before anyone has typed. The "I do not know" when the question is outside what the model can answer. The error when a tool call times out. The half-answer when the stream dies at sentence three. Teams build the happy path in a day and leave these for "later," but later never comes and these are the states a real user hits most. An edit affordance and an undo beat trying to be right every time, because they admit the thing everyone in the room already knows: it will sometimes be wrong, so make wrong recoverable instead of pretending it will not happen.
How we build it
Built for how AI actually behaves: live, fallible, and worth verifying.
An AI interface is not a form with a slow submit button. These are the patterns that make one feel alive and trustworthy instead of broken and suspicious.
Streaming that feels alive
Nobody should stare at a spinner wondering if it crashed. The answer appears as it is written, piece by piece, so the user is reading within a second and can tell the system is working. Perceived speed is most of real speed, and streaming is where you win it. A response that arrives all at once after ten silent seconds feels slower than one that starts in one and finishes in twelve. The number that matters here is time to first token: how long until anything shows up on screen, not how long the full answer takes. Users experience that first moment as the entire wait. Streaming does not make the model faster, the total time is the same, it makes the wait feel shorter by trading one long silence for a steady drip the eye can follow. We wire it on the right streaming transport, with the cursor and the partial text rendering as the bytes arrive, not buffered and dumped at the end.
The agent works in the open
When the system pauses to search, call a tool, or take a step, the user should see it happen. 'Looking that up' is reassuring. Eight silent seconds is indistinguishable from a crash, and that is exactly when people give up and refresh. We surface what the agent is doing so a pause reads as work, not as failure. Concretely: a step indicator that names the tool being called, the query it ran, and a row that ticks from running to done. The user does not need the raw logs, they need proof the machine is busy on their behalf. A labeled pause is patience. An unlabeled one is the moment they reach for the refresh button and fire a second request you now have to reconcile.
Failure is a state you designed, not a dead end
Timeouts, refusals, half-answers, rate limits, and plain wrong outputs all happen, and they happen often. We design the retry, the fallback, the partial result, and the honest 'here is what I could do.' The user hits a recoverable moment, never a blank screen and a shrug. The difference between a toy and a product is what happens on the bad path. Each failure gets its own shape: a timeout offers retry without retyping the question, a refusal explains the boundary instead of returning nothing, a stream that dies mid-sentence keeps what arrived and marks it incomplete rather than throwing the whole answer away. The error state is not the edge of the product. For an AI product it is a main screen, and we treat it like one.
Latency you hide on purpose
Some of the wait is unavoidable; the model takes the time it takes. So we spend that wait well: optimistic updates, skeletons that hint at the shape of the answer, streaming the first useful part while the rest is still forming. The product stays responsive even when the model is thinking hard, because the user is never left with nothing to look at. Latency is a UX problem before it is a backend problem. You cannot always cut the seconds, but you can change how they feel: show the user's own message instantly, render a placeholder that matches the answer's layout, and lead with the part that finished first so reading starts before generating ends. The model is slow. The interface does not have to feel slow.
An interface that earns trust
People act on answers they can check. Citations back to the source, the ability to edit and undo, a visible confidence cue, and a human-in-the-loop checkpoint where the stakes are real. Trust is not a friendly tone of voice; it is whether the interface lets the user verify before they commit. Build that in and people rely on it. Leave it out and they second-guess every answer. A bare answer with no source is a thing you either swallow whole or reject whole, and most people learn to reject. An answer with a link they can open, a quote they can check, and a confidence cue that does not overclaim is a thing they can work with. The cue does not need to be a percentage. 'Based on these three documents' or 'best match, with alternatives' tells the user where to look and what to trust, which is the only question they were ever asking.
The input is half the interface
Most teams obsess over the answer and ship a plain text box for the question, then wonder why users phrase things the model cannot handle. The input is where the conversation is won or lost. We design it to carry intent: an empty state that shows what good questions look like instead of a blinking void, the ability to edit and resend a prompt in place rather than retyping it, a stop button so a wrong turn costs one click instead of a full wait, and a clear handoff for attachments or context the model needs. Give the user a real way to ask, and you cut the wrong answers before the model ever runs.
"Users never see your model. They see a text box, a cursor, and how it behaves when something goes wrong. That is the product, and most teams build it last."
What you get
The face your AI product deserves.
The interface, wired to your model, built to handle the messy reality of generation. Handed over clean, with the components to keep building on.
- The chat, copilot, or agent interface your product needs, designed end to end
- Streaming wired to your model or API, so responses appear as they are generated
- Visible tool-call and step states, so the user sees the system working, not frozen
- Error, retry, and fallback handling for timeouts, refusals, and partial answers
- Trust features where they matter: citations, edit and undo, human-in-the-loop checkpoints
- A reusable component layer for AI interactions, so the next feature is faster to build
- The full repository and documentation, so any developer can extend it
Have a model that works but a front end that does not do it justice? Show us what it does today and we will tell you what the interface is costing you.
Invoke usIs this the right call
When this fits.
Good fit
- You have a model or an API that works, and a front end that does not show it off
- Your AI feature gets 'is it broken?' feedback because it freezes silently while it thinks
- You are adding AI to an existing product and the interface is an afterthought
- Users do not trust the output, and you suspect the interface is the reason
Wrong call
- You do not have a model or pipeline yet. Start with the brain (our AI Solutions work), then come back for the face.
- You need a plain form or a standard screen with no AI in it. That is web app work, not this.
- You want a flashy demo with no intention of shipping. We build interfaces for production, not for the pitch.
Deployment and scale
Streaming, reconnecting, and failing gracefully in production.
Streaming is easy in a demo and hard in the real world. Connections drop, tabs sleep, networks wobble, and the stream has to survive all of it without losing the user's place. We build the reconnection and resume logic so a flaky connection does not throw away a half-finished answer.
Rate limits and backpressure are a UX problem, not just a server error. When the model is overloaded or the user is going too fast, the interface should slow down gracefully, queue, and explain, not throw a red box and lose the conversation. We design those limits as part of the experience.
As usage grows, the interface holds up because the heavy lifting is structured: streaming is incremental, state is predictable, and the expensive calls are the ones the user actually needs. The front end does not melt the first time the product gets popular.
What we settle before we begin: what model or endpoint sits behind it, which failure modes are most likely, and how much a user must be able to trust the output before they act on it. Everything else follows from those three.
Tell us what your AI does, and what it feels like to use today.
Describe the model or the feature, who uses it, and where the experience falls down: the silent pauses, the dead ends, the moments people stop trusting it. We will tell you what the interface should do, and the shortest honest path to building it.