AI spend optimization and token cost reduction for business. CompletionPrism™ optimizes work, not tokens: it prices what each task costs to finish, rework included, and runs every step on the right model across Anthropic, OpenAI, Google and xAI.
Patent Pending. Every wrong AI answer has a labor cost attached.
CompletionPrism™ optimizes AI spend by optimizing work, not tokens. The expensive part of AI is not the token bill. It is the labor cost of fixing the answer when the wrong model was used. CompletionPrism™ prices what each task costs to finish and runs every step on the right model, across every provider you approve, including Anthropic, OpenAI, Google and xAI, so better work gets done at optimal cost. It runs the work through its own agent harness, so it can choose a different model for each step of the same task. Before each step is sent it works out what it will cost to finish that piece of work on every approved model, counting the attempts it will take and the person's time, then runs it on the cheapest path that still holds quality: route down, route up, stay, or stop.
Providers charge per request. Work is done per task, and a task takes as many attempts as it takes. Capped to a cheap model, one hard task took four attempts for $0.12 plus one hour of human rework at $100.00, a real cost of $100.12. The right model, first try, cost $0.50 in tokens and six minutes checking the answer, $10.50 in all, so the more expensive model was far cheaper overall.
CompletionPrism™: the right AI model, less rework, lowest cost to finish.
Before every step is sent, CompletionPrism™ makes one of four moves. Route down: a cheaper model gives the same result at less cost with no added rework. Route up: a stronger model costs more per call but less to finish, because it avoids rework. Stay: switching would not reduce rework or total cost. Stop: another attempt is not worth the rework or cost. Most AI cost tools only route down.
In a controlled test inside Claude Code, routing each step to the right model passed 100% of quality checks twice against 94.4% for one model throughout. Model calls cost 18% more, rework fell to zero, and the total cost to finish fell from $84.08 to $5.79. Pass the quality checks and cut rework cost by 90% or more.
CompletionPrism™ is an AI spend optimization and token cost reduction product from illuminis. It optimizes AI spend by optimizing the work, not the tokens. The unit it manages is the finished task, not the single request. Before every step is sent, it prices what the task will cost to finish on every model the company approves, across Anthropic, OpenAI, Google and xAI. That price counts five costs: token spend across every attempt, the human rework a wrong answer causes, switching models partway through, hidden reasoning charges, and the conversation re-read on every turn. It then runs the step on the model with the lowest cost to finish, and every answer carries a receipt showing what it cost and what it saved.
CompletionPrism™ runs AI work through its own agent harness: the loop that plans a task, calls the models, uses the tools and checks the result. Because it runs that loop itself, rather than only watching another tool, it can choose a different model for each step of the same task. Strong models make the decisions, fast and inexpensive models carry out the routine steps, and long conversations stay with the model already holding them so the provider's discount on cached context is kept. Before each step it routes down, routes up, stays or stops, whichever finishes the task for the least total cost, and it bills every call to the company's own provider accounts. Teams that keep Claude Code or Codex can point them at CompletionPrism™ with one environment variable.
Measure cost per finished task, not price per token. Most AI spend goes on retries, human rework and re-reading long conversations, so real AI spend reduction comes from running each step on the model that finishes it cheapest, keeping long conversations on the model already holding them, and stopping attempts that are not worth making. CompletionPrism™ does this automatically on every step, across every model you approve, and shows the saving on a receipt with every answer.
Token cost reduction comes from three habits: send routine steps to fast, inexpensive models, keep long conversations with the model already holding them so cached context stays discounted, and watch for reasoning tokens some models bill but never show. Token cost optimization on its own can backfire, because a cheaper model that needs extra attempts and human fixes costs more overall. CompletionPrism™ reduces token spend and rework together, so the whole AI bill falls, not just the token line.
Token cost optimization lowers the price paid per request. AI spend optimization lowers the total cost of getting the work done, which also counts extra attempts, human rework and the cost of switching models. A company can cut its token bill and still raise its AI spend if people end up redoing more work. CompletionPrism™ optimizes AI spend as a whole, and token costs fall as part of it.
Measure what it costs to finish the work, not the price of each request. Most AI cost goes on retries, rework and re-reading long conversations, none of which a per-token price comparison shows. CompletionPrism™ prices the full cost to finish each step on every approved model before the request is sent, then runs it on the cheapest path that still passes the quality bar.
AI cost to completion is the total cost of getting a piece of work finished: the tokens across every attempt, the person’s time spent catching and fixing a wrong answer, the cost of switching models mid-conversation, reasoning the model bills but never shows, and the conversation re-read on every turn. It is the number CompletionPrism™ predicts and optimizes.
No. A cheaper model that needs four attempts and an hour of human rework costs far more than a stronger model that gets it right first time. In a controlled test, routing each step to the right model cost 18% more in model calls but cut the total cost to finish from $84.08 to $5.79, because rework fell to zero.
Routers and gateways choose a model on price, speed, availability or difficulty for a single request. CompletionPrism™ predicts the cost to finish the whole piece of work, including the human rework a wrong answer causes. It then chooses, before the request runs, whether to route down, route up, stay on the current model, or stop.
Before every step is sent, CompletionPrism™ makes one of four moves. Route down: a cheaper model gives the same result at less cost with no added rework. Route up: a stronger model costs more per call but less to finish, because it avoids rework. Stay: switching would not reduce rework or total cost. Stop: another attempt is not worth the rework or cost. Most AI cost tools only route down.
Monitoring tools report what AI spend was after the money is gone. CompletionPrism™ changes the number before it is spent, by choosing the right model for each step, and every answer carries a receipt showing what it cost and what it saved.
Lowest cost to finish is the CompletionPrism™ promise. It means optimizing the total cost of getting a piece of AI work finished and correct, not the price of a single request. That total counts every attempt, the human time spent fixing a wrong answer, and the hidden charges a price list leaves out.
AI rework is the time a person spends noticing that an AI answer is wrong, working out why, and doing the work again. It never appears on the AI invoice because it is paid through payroll, yet it is usually the largest cost. One hour of rework at $100 an hour dwarfs the few cents a cheaper model saved.
AI IS SMART. REWORK IS STUPID.™ is the CompletionPrism™ campaign line. Modern AI models are capable, but pushing people onto the cheapest model makes them redo work, and paying people to fix avoidable mistakes is the most expensive way to save money on AI.
Agents and coding assistants run many steps, and each step can use a different model. Use a strong model where a decision is made and a fast, cheap model where work is carried out, and keep long conversations with the model already holding them. CompletionPrism™ does this per step, and works beside Claude Code and Codex with one environment variable.
Stop measuring price per token and start measuring cost per finished task. Route each step to the model that finishes it cheapest, avoid switching models mid-conversation so cached context is not thrown away, and account for reasoning tokens that some models bill but never show. CompletionPrism™ prices all of this before each request and bills every call to your own provider account.
Choose on the expected cost to finish, not the sticker price. A model that is cheap per call but needs three attempts is not cheap. CompletionPrism™ makes this choice automatically on every step, across every model you approve, and prices new models as soon as they are released.
An AI spend optimization platform lowers what an organization spends on AI while keeping quality. Most tools report spend after the fact, or chase token cost reduction alone by routing requests to cheaper models. CompletionPrism™ is built around cost to completion: it predicts the full cost to finish each step, including human rework, and chooses the right model before the money is spent.
In controlled testing it passed every quality check while cutting rework cost by 90% or more. Savings depend on your work, models and labor rates, so CompletionPrism™ can first measure your own traffic, with nothing rerouted, and report the projected annual saving with its assumptions shown.
It routes across the providers you approve, including Anthropic Claude, OpenAI GPT, Google Gemini and xAI Grok. New models are priced as they ship and used as soon as they win. Every call is billed to your own provider account.
No. CompletionPrism™ works in the command line, a web app, a desktop app for Mac and Windows, and a Visual Studio Code extension, with a mobile app coming soon. Developers can also point Claude Code or Codex at it with one environment variable.
Not by default. It keeps structural signals and token counts rather than prompt content, never stores a provider credential, and fails open: if it cannot make a decision, your request goes through unchanged.
CompletionPrism™ is patent pending. It is built by illuminis.