FinchOp is a token compression layer for enterprise AI. Drop it between your apps and any LLM provider — OpenAI, Anthropic, Gemini, or Bedrock. Cut LLM API costs by 78–82%. Average enterprise savings: $476,000/year. Zero impact on output quality.
73% of enterprises already spend over $50,000/year on LLMs. Budgets are growing 75% this year. But the spend is unattributed, unoptimized, and mostly invisible.
Every AI API call sends thousands of unnecessary tokens — redundant system prompts, full document dumps, repeated context, verbose instructions. This isn't intentional; it's how most AI agents are built by default.
3–10× more tokens than neededFinance sees a single monthly bill from OpenAI or Anthropic. There is no breakdown by team, workflow, agent, or business outcome. 84% of organizations discover more AI tools than expected during audits.
$500K–$2M in hidden tool waste (avg)The problem multiplies with every new AI initiative. Inefficient token patterns run across millions of daily calls. And as LLM prices fall, usage grows faster — total bills keep climbing regardless.
86% of AI budgets increasing in 2026A single integration point. No model changes. No retraining. No prompt rewrites.
FinchOp parses your prompt and extracts only the semantically necessary content — vendor fields, intent signals, structured data. Boilerplate and redundancy are stripped before the API call is made.
On multi-turn conversations and agentic workflows, FinchOp tracks what the model already knows and sends only what is new or changed — never re-sending information from prior turns.
FinchOp enforces JSON-only response schemas for every call, eliminating the prose explanations and verbose reasoning that inflate output token bills by 60–70%.
Ready to stop paying for tokens you don't need?
FinchOp deploys in 2 weeks. Average payback: under 60 days.
Existing tools solve pieces of the problem. FinchOp is the only solution that addresses token waste at the source.
| Feature | 🐦 FinchOp | Cloud FinOps Tools e.g. Apptio, CloudHealth |
Native LLM Features OpenAI caching, Batch API |
Manual Prompt Eng. In-house optimization |
|---|---|---|---|---|
| Reduces token usage automatically | ✓ 78–82% | ✗ Not applicable | ~ 15–50% | ~ 20–40% |
| Works across all LLM providers | ✓ All major providers | ✗ Cloud infra only | ✗ Provider-specific | ✓ Manual effort |
| Real-time cost attribution per workflow | ✓ Built-in dashboard | ~ Cloud only | ✗ Aggregate only | ✗ No visibility |
| No code changes in your app required | ✓ Drop-in middleware | ✓ | ✗ Code changes needed | ✗ Full rewrite |
| Shadow AI & spend detection | ✓ Full audit trail | ~ Limited | ✗ | ✗ |
| Output quality guaranteed | ✓ Schema contracts | ✓ Not relevant | ~ Best effort | ~ Depends on skill |
| Typical time to value | 2 weeks | 3–6 months | 4–8 weeks | 6–12 months |
See the numbers for your own workflows.
Request a private demo — we'll model your exact AI spend and show you the savings estimate.
These numbers are based on GPT-4o pricing ($5/1M input · $15/1M output) and typical enterprise usage volumes.
AI agent reads invoices, extracts fields, validates data, and routes for approval. Standard agents send the full document on every call — FinchOp sends only the extracted fields.
AI classifies tickets, pulls relevant context, and drafts responses. Most agents re-send full conversation history and policy handbooks on every turn — FinchOp sends deltas only.
AI compiles multi-source data, identifies anomalies, and generates executive summaries. Standard agents dump entire datasets — FinchOp pre-aggregates and sends structured deltas.
Join the waitlist or request a private demo. We'll personally reach out within 48 hours.
FinchOp is an AI FinOps middleware platform that reduces enterprise LLM API token costs by 78–82% through semantic compression, context delta management, and structured output contracts. It requires no code changes and deploys in 2 weeks.
AI FinOps is the practice of applying financial governance and optimization to enterprise AI and LLM API spending. It includes cost attribution by team and workflow, token usage optimization, ROI measurement, and shadow AI detection. The discipline mirrors cloud FinOps, which emerged around 2013–2018 as cloud bills became unmanageable and grew into a $6B+ market. Key acquisitions: Apptio ($2.4B, IBM 2019), CloudHealth ($500M, VMware 2018). AI FinOps is at the same inflection point in 2026: 85% of enterprises cannot prove AI ROI (Deloitte, 2025), 73% spend over $50K/year on LLMs (a16z, 2025), and 86% are increasing AI budgets this year.
FinchOp intercepts LLM API calls at the middleware layer and applies three techniques before forwarding to the provider. First, semantic compression: it parses the prompt and removes redundant boilerplate, re-sent context, and verbose instructions, reducing input tokens by 70–80%. Second, context delta management: on multi-turn conversations, it tracks what the model already knows and sends only new or changed information, eliminating history re-send overhead. Third, structured output contracts: it enforces JSON-only response schemas, eliminating verbose prose in model outputs and reducing output tokens by 60–70%. Combined, these techniques achieve 78–82% total token reduction with no measurable impact on output quality.