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Terse

Cut enterprise inference spend by 60% without losing intelligence

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Engineering executives are watching quarterly infrastructure budgets disintegrate as autonomous software agents consume millions of billable tokens on redundant conversational filler and markdown formatting.

Terse deploys as a zero-latency semantic gateway that autonomously compresses prompt context and model outputs by 60% without degrading reasoning precision or code quality. By pruning conversational pleasantries and converting agent instructions into high-density machine dialect, engineering organizations dramatically accelerate inference speeds while slashing monthly provider invoices. Engineering leadership gains real-time observability into token waste, payload density, and granular team-by-team inference economics.

We are establishing the foundational bandwidth and governance protocol for the autonomous machine-to-machine economy.

COMMENTS — Community Discussion
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#Cloud#DevOps#Fintech#Data
Problem
  • When I deploy autonomous coding and workflow agents across our engineering teams, I want to accelerate feature shipping, but runaway conversational bloat causes our monthly API bills to surge past $35k while adding 8 seconds of latency per loop.
  • Why Now: The explosive adoption of autonomous coding systems like Claude Code and continuous multi-agent execution loops has transitioned LLM usage from simple single-shot queries to persistent hundred-turn threads that multiply token costs exponentially.
  • When I monitor long-horizon multi-agent tasks, I want to preserve critical execution state, but unnecessary pleasantries and verbose markdown overflow context windows, resulting in amnesia and $10k in lost developer productivity.
  • When I defend unit economics during board meetings, I want to demonstrate compounding software gross margins, but unpredictable 300% inference overages erode product profitability by 20% every quarter.
  • Existing Alternatives: Developers manually craft brittle system prompt instructions begging agents to be concise, maintain fragile regex post-processors, or enforce strict character limits that inadvertently truncate critical code logic.
Solution
  • High-Level Concept: Cloudflare for Machine-to-Machine Token Economics
  • Semantic Context Pruning: A sub-5ms proxy engine that strips non-essential syntax, pleasantries, and redundant context before model processing.
  • Algorithmic Shorthand Engine: Converts multi-turn instructions into compressed machine dialect, preserving 100% logic and reasoning while cutting payload size by 60%.
  • FinOps Telemetry Console: Granular visibility into agent verbosity, per-service spend attribution, and automated tripwires that halt recursive execution loops.
Distribution
  • VP of Engineering and Heads of Platform at growth-stage AI-native software companies spending over $15k per month on inference APIs.
  • Drop-in extensions and configuration recipes for major agent orchestration frameworks and CLI development environments.
  • Open-source prompt waste CLI tool that audits an engineering team's historical API log exports and generates an instant dollar savings report.
  • Direct technical sales targeting engineering teams displaying high commit velocity on agentic developer tool repositories.
Pricing
  • Value Ladder: Developer Tier at $0 for up to 500k pruned tokens monthly; Team Gateway at $249 per month for up to 50M tokens; Enterprise Tier at $1.8k per month with dedicated VPC edge nodes, custom AST dictionaries, and SOC 2 compliance.
  • One Metric That Matters: Net Monthly Inference Dollars Saved across active production gateways.
  • Market Sizing: 22k mid-market AI companies spending an average of $40k annually on inference represents a SAM of $880M; capturing 1.5% in Year 1 yields $13.2M in annual recurring revenue.
Scale Costs
  • Global low-latency edge deployment across 35 regions to guarantee under 5ms token transformation overhead.
  • Continuous automated AST validation suites to mathematically verify zero semantic drift or logic degradation across edge test matrices.
  • Enterprise security infrastructure including SOC 2 Type II compliance, zero-data-retention guarantees, and encrypted memory enclaves.
Expert Opinions
Avg: 9.6
  • Enterprise Cloud FinOps Principal at a Fortune 500 Infrastructure Firm
    9.6
  • Lead Systems Architect in Autonomous Agentic Workflows
    9.4
  • Managing Director at an Enterprise Infrastructure Venture Capital Fund
    9.7
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