We optimize AI context architectures for engineering teams running multi-turn agentic workflows and seat subscriptions (Claude Pro/Max, ChatGPT Team). We configure your working context under 64K tokens with cache prefix stability, preventing context bloat, 429 rate-limit stalls, and runaway model invoices.
Long agent runs accumulate stale Bash stdout, tool logs, and outdated turns, degrading reasoning quality.
Multi-turn subagents exhaust Claude Pro/Max and ChatGPT Team limits prematurely, stalling execution.
Naive conversation slicing breaks prefix alignment, destroying Anthropic 5-minute and OpenAI prompt caching.
Every subsequent turn pays the quadratic token penalty; latency climbs to 25–40 seconds per turn.
Context-compression extensions actively keep active memory compact, maintaining sharp attention fidelity.
Prompt prefixes and tool definitions stay locked and immutable, maximizing cache read discounts.
The newest turns are protected verbatim; output capacity is guarded against generation truncation.
Specialist engineers audit, customize, and maintain your context pipeline at 30% of AI spend.
How our Pi context-compression extensions and runtime router keep multi-turn agent systems fast, responsive, and cost-effective.
Dynamic history modifications invalidate Anthropic's 5-minute and OpenAI's prefix cache. Our engine strictly preserves identical system prompt and schema prefixes so subsequent turns hit provider prompt cache (60–90% hit rates).
Large tool results (Bash logs, AST dumps, SQL queries) are compactly summarized into high-signal key facts while maintaining a lossless read-only disk reference to full output, keeping agent prompt buffers clean.
The newest two conversation turns and active tool calls are strictly protected from compaction. An 8,192 token output capacity is reserved, ensuring models never hit generation truncation walls.
We don't just hand you software. Dedicated AI optimization engineers configure, tune, and monitor your pipelines.
We analyze your multi-turn agent loops, subagent hierarchies, tool schemas, and prompt churn points to identify where context bloat and cache invalidation occur.
We deploy and configure Pi context-compression extensions, setting calibrated soft budgets (<64K tokens) with custom protected turn rules tailored to your application.
We restructure dynamic prompt sections to keep common prefixes immutable, maximizing Anthropic 5-minute and OpenAI prompt cache read discounts.
We customize tool output minifiers, extraction heuristics, and compaction triggers specifically to your application's data schemas and codebase conventions.
Monthly engineering review of token ratios, request latencies, and cache metrics with ongoing prompt recalibration as foundation models evolve.
Download our standalone CLI verification kit to measure baseline vs. optimized token counts, cache hits, requests, and quality acceptance before committing.
Zero dependencies • MIT License • Strict Cohort Matching
$ python3 audit.py baseline.csv optimized.csv --pretty
{
"status": "PASS",
"quality_pass": true,
"cohort": { "matched_tasks_count": 50 },
"deltas": {
"input_tokens_delta_pct": -68.4,
"cache_hit_pct_delta": +72.1,
"requests_delta": -14,
"cost_savings_pct": 64.2
}
}
Note: For flat-rate subscriptions (ChatGPT Team, Claude Max), context compression expands throughput and removes rate-limit barriers. For pay-as-you-go APIs, it slashes invoice costs.
No opaque retainers. Pay exactly 30% of your monthly AI spend, regardless of volume, starting from $69/month to a $269,000/month upper cap.
Input your team's total monthly AI API or subscription spend:
How savings, efficiency, and capacity expansion are strictly proven against identical task cohorts.
Direct BPE tokenizer measurement of uncached prompt tokens across identical tasks. Target: active context compressed under 64K tokens with zero loss of task fidelity.
Provider telemetry audit verifying prompt cache hits (Anthropic cache_read_input_tokens, OpenAI cached_tokens) reach 60%–90% on multi-turn pipelines.
Zero regression on your evaluation benchmarks. We validate functional parity between uncompressed and optimized runs before certifying savings.
Explore our production systems, context architectures, and autonomous automation deliverables.
Under-64K working context engine with cache prefix stabilization for AI subscriptions and agentic systems. Human engineering included with transparent 30% pricing.
ADB-driven autonomous Android profile capture and AI agent extraction into normalized, typed JSON. 4×4 dataset gallery, volume pricing, and Palantir brief pack.
Tell us about your AI workflow, current subscription or API spend, and where your agents hit context limits.