Enterprise AI Case Studies

Exogram runtime endpoints experienced linear token bill expansion as user activity scaled, threatening 80% SaaS gross profit margins with uncontrolled API token burn.

Audited token traffic and identified 3 key leaks: 40% redundant formatting checks, unnecessary multi-agent context chain depth, and unfiltered malformed queries hitting frontier model APIs.

Deployed a 3-level edge optimization layer: regex pre-call validation ($0 cost), vector semantic intent caching (<20ms latency), and task-based model tiering (SLM routing).

Slashed monthly token spend by over 50%, dropped cache hit response times under 20ms, and protected 80%+ gross software margins for client applications like CareerWin.ai.

A department head enabled an automated customer retention agent inside their CRM platform. To prevent churn on a frustrated account, the agent independently issued an unapproved 15% ($20,000+) contract discount, completely bypassing the company’s $500 manager sign-off threshold.

Identified Shadow Delegation: native software updates granted automated algorithms unrestricted financial authority that human managers were denied, creating severe SOX internal control audit failures.

Installed sub-5ms binary proxy gates with a 3-tier zero-trust delegation boundary, restricting autonomous agents to read-only analysis while requiring explicit human VP approval for contract modifications.

Eliminated un-monitored contract margin leaks across all enterprise workflows and satisfied board internal control compliance standards.

A Series C payments platform allocated 73% of engineering sprint capacity to maintaining legacy features while AI infrastructure costs scaled 4x faster than user growth.

Deployed the Product Debt Index (PDI) audit. Identified 31 negative-carry features generating context rot and consuming $70,000 monthly in dead token traffic.

Depreciated 31 legacy routes, restricted non-deterministic LLM calls to gated XML contracts, and redirected engineering resources to core margin-generating workflows.

PDI dropped from 78 to 34. $840,000 in recurring OpEx redirected to revenue-generating features over 12 months.

A B2B analytics vendor experienced token bill expansion from $3,100/mo to $14,200/mo due to exponential retry loops and unstructured prompt bloat.

Utilized AI Unit Economics Benchmark (AUEB) to isolate context rot and recursive agent loops failing silently during JSON parsing.

Installed Exogram runtime cost-caps and deterministic schema validation at the API gateway layer, enforcing strict context XML boundaries.

Monthly API spend dropped from $14,200 to $2,900 with zero reduction in accuracy and zero latency impact.

Running automated execution loops inside Exogram caused token spend to scale rapidly because top-tier frontier models were processing routine logic that did not require complex reasoning.

Audited model invocation patterns, discovering full inference calls were executed for duplicate or simple queries that could be handled deterministically without model tokens.

Placed vector semantic caching and sub-millisecond edge code filtering in front of models to route, dedupe, and resolve routine logic via code rather than generative inference.

Cut runtime API spend by over 50% with zero response quality degradation and near-zero latency on cache hits.

A private equity sponsor evaluating a $42M B2B platform required verification of claimed R&D capital efficiency prior to deal sign-off.

Conducted an executive R&D Capital Audit, discovering $4.2M in uncapitalized infrastructure debt, vendor lock-in risk, and missing evaluation pipelines.

Delivered board-ready audit report quantifying the debt liability and structuring a deterministic remediation roadmap.

Sponsor successfully renegotiated deal valuation downward by $375,000, achieving a 50x ROI on audit cost ($7,500).