AI advice from the team that runs the batch lanes
We help businesses adopt AI, audit what they already run, and cut the cost of inference. Not everything a team pushes through a model is conversation, and queue work belongs on a batch lane.
Spend to cut, and work worth starting
An AI review that only hunts for savings misses the work AI could start to do. We look in both directions on every engagement.
Cost savings on the AI you already run
We review the inference you run today: the models, the routing, the prompts, and the bill behind them. Queue work that nobody waits on does not need a real-time lane, and we find it for you.
- Model and provider choice reviewed per workload
- Real-time calls that could run as scheduled batches
- Duplicate, retried, and abandoned inference you still pay for
New capability and better products
Cost is not the only question. We also look for the work AI could do that you do not attempt today, because it looked too expensive or too slow to be worth it.
- Product features that become viable at a batch price
- Manual review and back-office work that AI can absorb
- Data you hold but never process, because the volume was too high
Batched inference costs less than real-time inference
That single fact is the spine of this practice. A generic AI consultancy can tell you to use a cheaper model. We can also change how the work is bought.
We are not advisors who read about this. We build and operate Batchrouter, the platform that routes batch inference to the cheapest eligible provider lane. So the advice comes from the team that runs the lanes, and any workload we identify as batch-suitable can move onto them without a second vendor.
You give up the instant answer
A batch returns inside a delivery window instead of immediately. For work nobody is sitting and waiting on, that costs you nothing.
Providers price that lower
A provider holds expensive capacity ready to answer a real-time call. Scheduled work fits around that, so providers offer their batch lanes at a lower rate.
We route to the cheapest eligible lane
You are not tied to one provider's batch price. Batchrouter compares eligible lanes for each batch and dispatches to the cheapest one that qualifies.
The controls stay yours
Batchrouter checks the privacy tier of a batch against every lane before it dispatches, so a cheaper lane never wins on price alone. Each lane also declares a region, and we publish it, but we do not enforce it as a residency guarantee today.
What you can engage us for
Each of these is sold on its own. Take the advice and stop there, or have us build what it recommends.
AI opportunity review
We work through your operation and map where AI can remove cost and where it can add capability you do not have today. You get a written, ranked list of candidates with our reasoning for each.
Works before you adopt AI at all.
Inference spend audit
We review the AI workloads you run now: models, routing, prompt and token behaviour, retries, and where the bill actually goes. We report what is well spent and what is not.
Take the report and act on it yourself.
Batch-fit assessment
We score each inference workload against a batch lane. Latency need, volume, data sensitivity, and provider eligibility decide it. We say plainly which work should stay real-time.
The batch question, answered on its own.
Workflow design and build
We turn a job into a workflow contract: the instructions, the output schema, the SLA tier, and the exception path when an item fails. Then we wire submission and delivery into your stack.
The delivery step, when you want it built.
Ongoing advisory
Model prices, provider capacity, and the catalog all move. We keep reviewing your routing and your workload mix so the decisions you made stay the right ones.
A standing review, not a project.
Not sure which one you need?
Describe the workloads you run, or the problem you want AI to solve. We will tell you which service fits and what it would cover.
Ask usWhich inference work suits a batch lane
A batch lane trades latency for price. That trade only works on some workloads. Here is our honest split.
Volume inference that nobody waits on
- Classification and labelling of tickets, content, or events at volume
- Field and entity extraction from messy documents
- Summaries and rollups of a queue, a backlog, or a reporting period
- Document review that produces findings and follow-up actions
- Embedding refreshes and retrieval-index backfills
- Evaluation runs and reprocessing of historical data
These already exist as workflow products, apart from evaluation runs and reprocessing, so a build starts from a contract instead of a blank page.
Work we will tell you to keep as it is
- Interactive chat where a person waits for the reply
- Inference inside a request path that must answer immediately
- Single low-volume calls with no repeat pattern
- Work that must stay on a provider you contract directly
We would rather move the workloads that fit than push the ones that do not.
The same four steps, however far you take it
An engagement can stop after step two. That is a complete piece of work.
Understand the operation
We learn what you run, what it costs, and what you wish you could do. Advisory work starts here, and so does every audit.
Find and rank the candidates
We separate the savings from the opportunities, then rank both by value and by effort. You get our reasoning, not just a list.
Test one workload for real
When you want proof rather than a report, we run one candidate end to end on a batch lane and show you the receipt.
Hand over or stay on
We leave the workflow definitions, the integration code, and a short runbook. You can take it from there, or keep us on a standing review.
Consulting questions
Start with a conversation
Tell us what you run today, or what you want AI to do for you. Write to hello@batchrouter.com and we will reply with a first read and the service that fits.