We start every engagement with a deep production architecture review. We map your workload, model your real cost per task under frontier only, self hosted, and hybrid, and deliver a written plan for the stack you should actually be running. Frontier APIs where they win, self hosted open models on your own GPUs where they don't. What hosting and rollout look like is a follow up conversation once the numbers are on the table.
This work is hard, and doing it well takes real attention, so we only take on a small number of clients at a time. The review is where every engagement starts, and it stands on its own whether or not we build the stack together afterward.
AI is going hybrid. The everyday work of an agent, parsing input, calling a tool, formatting a result, the 90 percent, runs cheaper and more privately on models you control. The hard 10 percent escalates to a frontier API. The teams winning in production are already building this split. Most haven't, because the routing, the evals, and the cost governance underneath are hard to get right. That's the work we do.
Every request goes to a black box you don't control. Prices change. Rate limits change. Models get deprecated. Your product's unit economics are set by someone else's roadmap, and switching later means rewriting the parts that work.
Prompts, tool calls, customer context, all traveling to third parties. Regulated buyers, enterprise procurement, and sovereign customers want inference that never leaves their environment. Frontier only stacks lose those deals before the demo starts.
Agents rerun steps. Context reloads on every call. Duplicate calls, retries, and context bloat scale with traffic. A $200 staging bill becomes $40,000 a month, and no one on the team can say where the money actually goes.
You should be able to move a workload between frontier and self hosted in a week, not a rewrite. We build stacks where that is true.
Every other layer in the AI stack makes money when you spend more tokens. Resellers, wrappers, orchestration platforms, all of them grow with your usage. We're built the other way. Our job is to make the bill smaller, and that's what we're paid to do. You pay the model providers directly. We never mark up a token. That incentive is the whole reason to trust the numbers below.
The core of what we do. A deep review of your current stack, your traffic, and your bill. We model cost per task under frontier only, self hosted, and hybrid, and deliver a written recommendation with the exact architecture, projected savings, and a migration path. Fixed fee. You keep the report either way.
Where every engagement starts, and where most of the value lands.
A blueprint for the split between frontier APIs and self hosted open weight models. Routing logic, model choices, serving software, GPU plan, evals, guardrails, and data plane. Written for your engineers to implement, or for us to implement with you.
Included in the review. Handed over as an asset you own.
If it makes sense after the review, we can stand up the stack with you. Self hosted serving in your VPC or on managed GPUs, routing, evals, observability, and cost governance in production. Scope, hosting, and timeline are a follow up conversation once the review is done, not a package we sell upfront.
Optional, and only when it's the right call for both sides.
The review is the product. It is scoped, fixed fee, and delivered in a written report you can act on with or without us. Because this work is hard and we do it properly, we only run a handful of reviews at a time.
We are vendor neutral by design. The right answer is almost always a mix, and it changes as models and prices move. We build the stack so that change is a config, not a rebuild.
A 30 minute call to look at your architecture, your traffic, and last month's inference bill. We tell you honestly whether a review will pay for itself, and what a realistic engagement looks like. If it isn't the right fit, we'll say so on the call.
The core of the engagement. Fixed fee, delivered as a written report. We model your cost per task under frontier only, self hosted, and hybrid, at today's volume, 10x, and 100x. You get a recommended stack with specific models, serving software, GPU plan, routing logic, and a migration path scoped in weeks, not quarters. The report is yours to act on with or without us.
Once the numbers are on the table we sit down and talk about what makes sense next. Sometimes that's your team executing the plan on their own. Sometimes it's us building alongside you, and we work out hosting, timeline, and scope together. It's a conversation after the review, not a package you buy upfront.
You've shipped on a frontier API. Usage is growing. So is the bill, and now procurement, security, or your CFO is asking questions you don't have clean answers to. This is the point where the stack needs to become yours.
If any of that is you, one call is enough to know if there's a real engagement here.
Continuous Labs is the services arm of ContinuousAI, an execution governance plane for agentic AI. Every engagement is staffed by the team building the infrastructure that kills wasted runs before they hit a model, so the savings you get modeled are the savings we know how to enforce.
Two provisional patents filed on execution-layer optimization. NVIDIA Inception member.
The cheapest token is the one that never generates.
A 30 minute call to scope the review. Bring your architecture, your traffic, and last month's inference invoice. We'll tell you what a review would cover for your stack and what it would likely find. What building and hosting look like is a follow up conversation once the review is done. We only take on a few clients at a time, so tell us where things stand and we'll be honest about fit.