Stop paying frontier prices for easy AI tasks.
Companies send millions of routine tasks to premium-priced AI frontier models, and share their IP and alpha along with it. We build you your own AI model that does the work as well or better, for a fraction of the price.
85% of it is routine work, priced like hard work.
Take a company that sends a million AI requests a day to score every call, read every claim file and pull the terms from every contract. A frontier model can do these. So can an open model a fraction of the size, at a fraction of the price.
“You essentially pay for intelligence twice…”
The cost that isn't on the bill.
Once in money. Then in the know-how every request carries to make the answer useful: your prices, who you trust, how you close a deal.
That's your alpha: what you know that your competitors don't.
And frontier models serve everyone. Including the companies you compete with.
Rented intelligence can't be your edge.
Keep your alpha. Then grow it.
Your alpha is what makes you better than your competitors. Keep it in a model you own, in your own cloud, and it learns a little more about how you win every month.
Only you can grow your alpha and outcompete others.
Your own AI model.
Built from your know-howRunning in your cloudOwned by you
A good model that knows your business beats a frontier model that doesn't. Your know-how, cut and polished a layer at a time, on the AWS or Databricks you already run.
- Routing picks the cheapest model that will get each request right
- Serving runs your model on GPUs in your own cloud, sized to your traffic
- Guardrails checks every answer and stops wrong or unsafe ones going out
- Fine-tuning teaches the model how your business decides
- Evals tests every new version against your own past work
- The right open model the open model that wins the benchmark on your work
- Your data your records and decisions, cleaned and labeled, in your account
About a tenth of the price. On the work you do most.
Routine requests go to your own model. Frontier models keep the hard problems. A router sends each request to the cheapest model that still gets it right.
The frontier model has never seen your business. So it guesses.
Score this call: why did they call, how did it end, and what do we owe them?
The caller sounds frustrated about a late delivery. Overall sentiment is negative. Consider following up with an apology and checking the order status.
It has never seen your business, so it guesses.
Reason: late chilled delivery, the second this quarter for a Gold customer. Outcome: unresolved, and they asked for a manager. Owed: free next-day delivery and a $10 credit under your late-order rule. Flagged as a churn risk.
It knows your policies, your customers and how you decide.
Does this supplier contract fit our terms?
A frontier model: The contract has standard payment terms and a termination clause. It would be sensible to have your legal team review it for any risks before signing.
Your model: No. Payment is 90 days; your policy caps suppliers at 60 (clause 4.2). Liability is uncapped, and your playbook requires a cap at twice the annual fees. Two changes sent to legal, with the clauses attached.
Is this claim ready to pay?
A frontier model: The claim appears to include the main documents. Please verify the details against the policy terms before approving payment.
Your model: Hold it. This is the third repair claim on the policy in 30 days, and the policy is 22 days old. Your rule sends that to investigations. Photos for item 2 are missing. A letter asking the customer for them is drafted.
A good model that knows your business beats a frontier model that doesn't.
Examples, to show the difference.
And a real one. Coinbase fine-tuned a small open model, Qwen3.5-9B, on its own data to prevent fraud. Its CEO, on how it did:
“…it out performed frontier models, at much lower cost and speed…”
A tuned model depreciates fast. The loop keeps it current.
For years the sensible call was to pay more for a general model, because keeping your own one current was a research project. Now a training loop that post-trains one of the best open models is ordinary engineering, on LoRA adapters and the managed GPUs of SageMaker AI or Databricks. So we build it into your account, beside serving, as part of the system.
- More dataProduction adds reviewed answers, corrections and escalations every day. We keep each one with its trace, and retrain that job's LoRA adapter once enough of them build up to move its score.
- New model releasesWe benchmark each new open model on your golden set soon after it comes out. When one beats your current base, we retrain every adapter on it from the versioned datasets, without a rebuild.
- Evolving tasksWhen a policy changes or a new job arrives, the golden set and the evals change with it, and a new job gets its own adapter. Nothing ships until it passes the regression suite, in shadow and then as a canary.
Four models race for every job you pick.
Your own model wins most jobs.
The few it loses are the hard 15%, which the router keeps on the frontier model. A small rented model costs less, and gets more wrong.
Now answering: Triage and route this ticket
rightwrong,on 16 of your own requests· price for 1,000 requests· time for one reply· example figures
- Your own model starts from the open model that wins on your workQwenGemmaMistralNemotrongpt-oss
- Teach and test withDeepSeekKimiGLMMiniMaxLlama
Open weights from Hugging Face, under each maker's own license. We check the terms with you, and leave out any your policies rule out.
Built like production software, not a demo.
Before we build, we agree three numbers for each job: the accuracy bar, p95 latency and cost per 1,000. Nothing goes live below them.
Find the work, score every model
We find the jobs your AI does thousands of times a day. Then we test every model on them, big and small.
- A golden set from your own work. Real requests for each job, each with the answer your experts signed off. Where there are no signed-off answers yet, domain experts we partner with write the first set.
- Every model sits the same test. Rented models through OpenRouter. Open models from Hugging Face, run in your account.
- Scored two ways. Exact match for labels and extracted fields. An LLM judge, calibrated against your experts, for written answers.
- What you see: accuracy, cost for each right answer and p95 latency, side by side. And which open model to start from.
Where it goes wrong: A test set full of easy cases. We sample by risk as well as volume, so the rare, costly cases are in it.
Bedrock Evaluations · SageMaker AITeach a model your business
We organize your data and your experts' reviewed answers, then fine-tune the open model that won the benchmark.
- LoRA adapters first. We train a small set of new weights on top, and leave the base model as it is. It costs far less than full fine-tuning, and one base model can carry an adapter for each job.
- Full fine-tuning only when it pays. If adapters stop improving on the benchmark, we train the whole model.
- Distillation, where a provider's terms allow. A larger model writes extra training examples, and your experts check a sample.
- A held-out test set. A new version ships only if it beats the last one on examples it never trained on.
- Open models that fit: Qwen, Gemma, Mistral, Nemotron or gpt-oss.
Where it goes wrong: A model that learns last year's answers. We test on the newest month of work, held back from training.
SageMaker AI training jobs · HyperPod for big runsRun it in your own cloud
Your model runs on GPU endpoints inside your AWS account or Databricks workspace, sized to your traffic.
- Continuous batching on vLLM. Many requests share each pass through the GPU, so each GPU serves far more traffic.
- Prefix caching. The part of the prompt that repeats is computed once and reused.
- Quantization, tested. Smaller weights fit a smaller GPU. We rerun the benchmark to prove accuracy held.
- Autoscaling, with a fallback. Endpoints grow and shrink with traffic. If one fails, the router sends its requests to the frontier model.
Where it goes wrong: A traffic spike hits a cold GPU. We keep a warm minimum, and the router sends overflow to the frontier model.
SageMaker AI endpoints (vLLM) · Bedrock Custom Model Import where supportedRoute every request
A router we build and tune on your own examples sends each request to the cheapest model likely to get it right.
- Before the call, it reads the request. A small classifier, trained on your benchmark, spots the job and how hard it is.
- After the call, it checks how sure your model was. It reads the model's token probabilities, and tests the answer against your schema and rules.
- If your model isn't sure, the request goes up to the frontier model you trust. We set that bar for each job, on the benchmark.
- Every escalation is kept. Once reviewed, it becomes the next round of training data.
Where it goes wrong: A confident wrong answer. Schema and rule checks catch what the probabilities miss. We audit a sample of what stayed with your model.
Our router · LiteLLM gatewayKeep it right, every day
The tests that chose your model keep watching it in production.
- Evals on live traffic. Each day, the benchmark's judges score a sample of real answers.
- Guardrails on the way in and out. They stop personal data, prompt attacks and answers outside your policy.
- Tracing on every request: which model and which version answered, what it cost, how long it took and how it scored.
- Drift raises an alert. When a job's score slips toward its bar, the alert names the job and the model version, and the training loop in step 06 decides what to fix.
Where it goes wrong: The judge drifts too. We check it against your experts every month.
Bedrock Guardrails · CloudWatch · MLflow tracingRetrain it when the evidence says to
A fine-tuned model starts to age the day it ships, as policies change, the work shifts and better open models come out. What lasts is the loop that retrains it, so we build that inside your account.
- Production feedback, kept with its trace. Escalated requests, answers your people corrected, failed schema or rule checks and the daily evals' low scorers are stored in your account, each with the version that answered.
- Curated before it trains anything. Your experts label the failures, and only examples you have cleared for training go in. We take out duplicates and personal data, weight the rare, costly cases, and version every dataset with its sources.
- Diagnosis before a training run. A changed fact is fixed in retrieval, a reworded rule in the prompt and a wrong action in the tool. Retraining is for judgment the model keeps getting wrong across many reviewed examples, or routine work still going up to the frontier model.
- A training run needs a trigger. A job's live accuracy falls below its bar, its share of escalations climbs, enough reviewed examples build up to move the score, or a new open model, benchmarked soon after its release, beats the current base. We agree each threshold with you at the benchmark.
- One adapter at a time. The run re-tunes that job's LoRA adapter on the new dataset version and leaves the base model and the other adapters alone. A better base model means retraining each adapter from the stored datasets, not a rebuild.
Where it goes wrong: A loop that learns from its own mistakes. Only reviewed answers become training examples, and a case in the test set never trains a model.
SageMaker AI training jobs · MLflow tracingPromote it in stages, with a way back
A retrained model reaches live traffic the way any careful release does, tested first and then exposed a little at a time.
- Regression tests, on a set training can't read. A new version must pass the benchmark again, beating production on the acceptance set and holding every older case inside the agreed p95 latency and cost per 1,000. We check it for memorized examples, and a named owner on your side approves the promotion.
- Shadow first. It answers a copy of live requests, scored beside production, and nobody sees its answers.
- Then a canary. The router gives it a small share of real traffic and widens that share in steps as its live scores hold.
- Every version in a registry, in your account, with the dataset that trained it and its eval report. The previous adapter stays loaded.
- Rollback on a line agreed in advance. If the canary's accuracy, latency or error rate crosses it, the router moves traffic back to the previous version on its own. We rehearse that before the first release.
Where it goes wrong: A new version that wins on average and loses a costly case. The rare, high-risk cases are scored on their own, and one failure there blocks the promotion.
SageMaker AI Model Registry · shadow tests · our routerBring us one workload. We'll print both bills.
Questions we get
What is AI inference engineering?
It's the work of making every AI request cheaper, faster and more accurate once a model exists. We choose the right model for each task and fine-tune open models on your data. We serve them in your cloud, and route each request to the cheapest model likely to get it right.
What do you mean by our alpha?
Ask any of your best people how they decide when to say no, what to charge, or who to trust, and you'll hear years of judgment calls no competitor can just go buy. Your own model learns it from your work and keeps it in your cloud.
Can an open model really beat a frontier model?
On narrow, repeated tasks, it can. A 2024 study, LoRA Land, tuned a small model for each of 31 tasks. The best beat GPT-4 on 25 of them. A frontier model has to be handed your rules on every call. A smaller model trained on thousands of your past decisions has learned them. It can match or beat the frontier model on that work, and it's faster and cheaper. We prove it on your own work before anything goes live.
Why fine-tune a model when it could just look things up?
We do both. Retrieval handles facts that change, such as a balance, a stock level or this week's price. Fine-tuning handles judgment that doesn't change the same way, such as how you score a call, when you escalate or what you never say. Putting the whole rulebook into every prompt works too, but then you're paying a frontier model to re-read it on every request.
When do you retrain the model, and when do you just change the prompt or the retrieval?
We diagnose the miss first, because each fix changes something different. Retrieval holds facts, so a new price or policy date is corrected at the source and is live once the index updates. The prompt holds instructions, so a reworded rule is edited, tested on the benchmark and released as a new prompt version, which can be swapped back. Neither touches the weights. Retraining changes what the model has learned. It is the fix for judgment the model keeps getting wrong across many reviewed examples, for a rulebook grown too long to send with every request, and for moving to a better open model. It costs a training run, a pass through the acceptance set and a staged release, so it starts only on a trigger agreed with you in advance.
Isn't a small rented model cheaper still?
Yes, per request, even per right answer on our example. It also gets more wrong: about 81% right against 96%. Where a wrong answer costs you a customer or a claim, that matters more than the price. We benchmark both and route each task to wherever it does best.
How big does the workload need to be?
Your own model has a fixed cost. On our illustrative example, that's about $16,000 a month to start. It pays for two GPU endpoints and part of the team that runs them. We add GPUs as traffic grows. Each request is 3,200 tokens in and 300 out. Against a large frontier model with caching on, it pays for itself at around 56,000 routine requests a day. Below that, renting is cheaper, and the benchmark will say so with your own numbers.
We already use frontier models. Do we have to stop?
No. Keep them for the hard problems. A router we tune on your examples sends routine requests to your own model and the rest to the frontier model you trust. You pay frontier prices only where frontier intelligence earns them.
Does our data leave our cloud?
For routine work, no. Your model, the router, the traces and the eval judges run in your AWS account or Databricks workspace. Only the hard requests you choose go to a frontier model. Through Bedrock, the model's maker never sees them. On some top models, AWS keeps them up to 30 days for review. Sent directly, the big providers' business terms say they don't train on your data by default. They keep it 30 to 55 days to check for abuse.
Which tools do you use on AWS and Databricks?
On AWS: Amazon SageMaker AI for training, with HyperPod for big runs, and for serving on vLLM. Amazon Bedrock for Evaluations, Guardrails and, where the model is supported, Custom Model Import. The router is ours, behind a LiteLLM gateway. On Databricks: AI Runtime (in preview) for training, Model Serving, Unity Gateway, Unity Catalog, and MLflow 3 for evaluation and tracing. Open weights come from Hugging Face. In the benchmark, we try rented models through OpenRouter.
Who owns the model?
You do. The fine-tuned model, its weights, the training datasets, the pipeline that retrains it, the eval suite, the routing rules and the code live in your accounts. No license to us to keep it running, and no lock-in to one model provider. The base model's own license still applies, so we pick one whose terms suit you.
How long until it's in production?
The benchmark typically takes two to three weeks and tells you what you'd save. A first workload is usually in production a couple of months after that. We agree the timeline after the benchmark, when we know your data.