AI Token Optimization Service
All AI Spend Tracked, Attributed,
Forecast, and Reduced.
BinaryWorks fixes the architecture producing your AI bill, so spend drops sharply, finance gets a number it can commit to, and the output stays exactly as good.
Where AI Spend Goes Wrong
Why AI Costs More Than Budgeted and
Nobody Can Explain Where It Went
Six failure points account for nearly every AI budget that overruns. They hit hardest where annual budgets are fixed, spend must be allocated, and procurement needs a number before anything is approved.
01 — “The invoice arrives. Nobody can say which team spent it.”
Why it happens: Everything runs through one shared key. There is no tag identifying the department, use case, or workflow behind each call, so the bill arrives as a single number.
What it costs: You cannot charge it back, allocate it, or defend it in a review.
02 — “Procurement needs a number. We cannot give them one.”
Why it happens: Budgets are set annually and approved in advance. AI bills by the token on a pricing model nobody matched to how the organization actually funds anything.
What it costs: Approved projects wait months because the cost cannot be committed.
03 — “The pilot cost four hundred a month. Rollout estimates fifty times that.”
Why it happens: Cost scales with tokens processed, not with users added. A pilot with twenty people and short documents tells you almost nothing about what full deployment costs.
What it costs: Projects that cleared the pilot get canceled at the budget stage.
04 — “We use the same model to classify a form and write a report.”
Why it happens: Whichever model was chosen first became the default for every task. Nobody revisited it, and routing simple work to a smaller model was never built into the design.
What it costs: You pay premium rates for work a cheaper model handles identically.
05 — “We send the whole document and instructions on every single call.”
Why it happens: The system prompt, reference material, and full history get resent with each request. Caching exists at every major provider and almost nobody has it configured correctly.
What it costs: You pay full price for identical content, thousands of times a day.
06 — “One user question turned into forty calls. Nobody set a limit.”
Why it happens: Agents chain calls, retry on failure, and loop until they finish. Nothing caps steps or spend per task, so a bug or an unusual input runs unchecked overnight.
What it costs: A single defect produces a bill nobody sees until month end.
Each of these is an engineering problem with an engineering fix. The next section maps all six.
What We Automate
Twenty Workflows We Automate
Across Five Sectors
These are the processes we are brought in to automate most often. Each one crosses systems, carries an audit trail, and runs today on people doing work nobody hired them to do.
Higher Education
Incomplete files are chased to completion and the student record updates without a handoff.
Healthcare
Documentation is assembled, submitted, and tracked, with clinical judgment escalated to the right clinician.
Nonprofits and Associations
Routine volume is processed and exceptions resolved without adding staff at peak.
Government
Every submission is checked against the criteria and prepared for staff decision.
Financial Services
Files are completed to policy, actioned across every system, and logged for audit.
Token Spend Audit
Every AI dollar traced and costed in 48 hours.
Most organizations cannot break their AI invoice down by team, use case, or workflow. The audit shows where the spend is going, what is avoidable, and what each fix returns.
- Spend broken down by team and use case
- Avoidable cost identified per workload
- Fixes ranked by savings and effort
Where the Savings Come From
Six Levers, in the Order We Apply Them
Order matters. The first two return most of the savings within weeks, at almost no risk to quality.
Repeated content billed at a fraction of full rate. Largest and fastest return, with no change to what the system produces.
Simple tasks moved to cheaper models. Second largest return, gated by quality testing before anything switches.
Fewer tokens sent per call. Meaningful reduction, and it usually improves accuracy by removing irrelevant material.
Structured formats and length limits. Output tokens cost several times input, so this returns more than teams expect.
Non-urgent work moved to a lower-rate queue. Applies to reporting, bulk processing, and anything nobody is waiting on.
The right commercial footing for each workload. Smaller share of the bill, but it is what makes spend predictable enough to commit.
How We Fix It
Six Capability Areas,
One Cost Roadmap
Each failure point above maps to an engineering fix, which is why AI cost control is an architecture problem rather than a procurement negotiation.
Six areas · one sequenced roadmap
/ 01 — Spend Visibility and Attribution
Every call is tagged to a team, use case, and workflow, then converted to cost per transaction. Finance gets a departmental breakdown and a unit cost it can set against the manual alternative.
Reduces: untraceable invoices and unprovable business cases
/ 02 — Caching
Repeated content stops being paid for repeatedly. System prompts, reference material, and common queries are cached at the right layer, which is the fastest and lowest-risk saving available.
Reduces: paying full price for identical tokens
/ 03 — Model Routing
Each task is matched to the smallest model that passes your quality tests. Classification and formatting route to cheaper models while complex reasoning keeps the capable one, with quality measured rather than assumed.
Reduces: premium rates on simple work
/ 04 — Context and Output Optimization
Prompts are trimmed, retrieval is scoped to what the task needs, and outputs are structured to stop over-generating. Work that does not need an immediate answer moves to batch processing at a lower rate.
Reduces: oversized requests and runaway responses
/ 05 — Agent Efficiency
Call chains are shortened, retry loops are fixed, and step limits are set per task. Agent workloads stop consuming several times the tokens of an equivalent single request.
Reduces: retry storms and unbounded loops
/ 06 — Deployment Model and Forecasting
Each workload is matched to the right commercial model, from usage-based to committed capacity to self-hosted where volume justifies it. Hard caps and a forecast built on real usage give procurement a committed number.
Reduces: budget overruns and stalled approvals
Practice Lead Session
Bring the AI Invoice
Nobody Can Explain.
Talk to BinaryWorks’ cost optimization practice lead. Walk in with the bill or the budget nobody will approve. Walk out with what we would fix, in what order, and why.
THE BINARYWORKS ADVANTAGE
Why Technology and Finance Leaders Choose Us
Most vendors optimize the model or resell the tokens. BinaryWorks fixes the architecture producing the bill, then gives finance a number it can commit to.
Engineering Production
Systems
Enterprise Builds
Delivered
Client
Satisfaction
Token Spend Audit
Turnaround
Hear From Our Customers
Results BinaryWorks Has Engineered
in AI Cost Optimization
Regional Health System Cuts Monthly AI Spend by 63% Without Changing Output
The same clinical reference material was sent with every request, and one model handled every task from document classification to summary drafting.
The Result:
- 63%Reduction in Monthly Spend
- 0Measurable Change in Output Quality
- 6 Weeksto Full Implementation
Private University Allocates AI Spend Across Departments for the First Time
A single shared account served every department. Finance could not charge costs back, and no unit would fund expansion without knowing its own share.
The Result:
- 100%of Spend Attributed to a Department
- 41%Reduction After Teams Saw Usage
- 0Additional Finance Headcount
Public Agency Gets AI Approved After Two Failed Procurement Attempts
Procurement rejected the project twice because no fixed annual cost could be committed. Usage-based pricing had no place in the approval process.
The Result:
- 1Committed Annual Figure for Procurement
- 94%Forecast Accuracy Over 12 Months
- 0Budget Overruns Since Deployment
Your Questions Answered
Published benchmarks put combined optimization at 60 to 85% of an unoptimized bill, and our engagements land in that range. The variable is how much low-hanging work exists. Systems with no caching and a single model for every task save most. Systems already routing and caching save less but gain forecasting and attribution, which is often the more valuable outcome.
Not if quality is measured rather than assumed. Caching, context trimming, and batching change what you pay, not what the model produces. Model routing does change which model runs a task, so every route is tested against real cases before it goes live and monitored afterward. Any route that fails the quality bar does not ship.
This is the most common blocker in your sector and it is solvable. Hard caps and per-team limits convert variable spend into a bounded number, and a forecast model built on your actual usage gives procurement a committed annual figure. Most organizations find the constraint was never the cost itself, only the inability to commit to one.
Development runs $75 to $300 per hour depending on scope and system complexity. Audit-first engagements let you scope the work before committing. Bundled packages across attribution, caching, and routing are available, as are FTE models for organizations needing ongoing cost engineering capacity.
Caching and attribution typically deliver in two to four weeks and account for the largest share of the return. Routing adds four to eight weeks because quality testing gates every route change. Savings appear on the first invoice after each change, so the engagement pays back before the later phases finish.
Prices did fall sharply through 2026, and usage rose faster. The bills that need attention are not driven by the rate card, they are driven by sending the same content repeatedly, running premium models on simple tasks, and agents looping without limits. Those costs persist at any price, and they compound as usage grows.
Sometimes. For a high-volume, predictable workload it can be cheaper, and it converts variable spend into fixed infrastructure cost that fits an annual budget. It also keeps data on your own systems, which several sectors need regardless. The tradeoff is that you take on the operations. We model both against your usage before recommending either.
Controls and dashboards stay in place and keep working. Usage patterns shift, though, and new use cases arrive without the optimizations built in, so a quarterly review keeps the gains. Teams can run this internally using our runbooks, or retain BinaryWorks for ongoing cost engineering and forecast updates.
Start Now and AI Spend Stays Inside Budget.
Send us one month of usage. You get the spend broken down by team and use case, the avoidable portion identified, and the fixes ranked by return.
