Solving the #1 Problem
in Production AI
Privacy-first LLM observability + Intelligent budget control
Your team can't answer "Why did our AI bill jump 10× last month?" Traditional APM tools force you to log prompts — a compliance nightmare. Skip logging and you're flying blind. DoCoreAI solves both. Full cost visibility. Zero prompt storage. Budgets that stay on track.
$ pip install docoreai
Works out of the box with
Enterprise AI teams are forced into an impossible choice
Your team ships an AI feature. Usage grows. Then one of two things happens — and neither option ends well.
"Why did our AI spend jump from $1K to $47K last month?"
"Which team is burning budget on inefficient prompts?"
"Did customer PII leak through that API call?"
"Why was our AI service down for 10 hours yesterday?"
Silent Token Waste
Default max_tokens
is set to 2,000+ as a safety net. Most responses need a fraction of that.
You pay for the ceiling — every single request.
No Native Guardrails
No LLM provider gives you budget pacing, spend prediction, or automatic intervention. You either overspend or build all of that yourself.
AI Pilots Stalling
78% of enterprises report bill shock from AI costs. 45% went 50%+ over budget. Projects get canceled not because AI failed — but because costs were uncontrolled.
Enterprise AI teams have been forced to choose between privacy and visibility — and left to solve cost control entirely on their own.
DoCoreAI eliminates all three problems. Scroll down to see how.
DoCoreAI runs alongside your app
and governs every LLM call — autonomously
It monkey-patches all active LLM SDK calls automatically at startup — in the same Python environment as your app. No imports. No wrappers. No changes to your existing code. DoCoreAI listens before and after each LLM call, extracts cost and token metadata locally, and applies governance, budget, and pacing decisions autonomously. Your app calls the LLM directly, unchanged. Only metadata reaches the cloud — prompts never leave your network.
Groq / Gemini SDK
Groq · Gemini · Bedrock
(same process)
Governance · Budget · Pacing
Dashboard · Alerts
DoCoreAI never sits in your call path. It monkey-patches the LLM SDK locally — listening before and after each call in the same Python process. Your app calls the LLM directly, unchanged. Only cost and token metadata is sent to the cloud. Prompts never leave your network. Ever.
Privacy-First Observability
Zero prompt storage by architecture. Your compliance team and your engineering team can both say yes.
- Metadata-only telemetry — no prompt content, ever
- Local-side instrumentation, nothing sensitive leaves your network
- PII detection at the edge before any API call
- Complete audit trails without compliance risk
- SOC2 certification in progress
Intelligent Budget Control
Replaces wasteful token ceilings with precise ML predictions. Paces your budget across the full day automatically.
- LightGBM prediction model learns your actual usage patterns
- Replaces default 2,000-token ceiling with precise estimates
- Soft limits guide AI to be concise — no hard truncation
- Daily pacing engine prevents budget exhaustion
- 40–70% projected cost reduction
Real-Time Cost Intelligence
Per-request cost tracking across every provider and team. Anomalies caught before they become bill shocks.
- Per-request tracking by team, feature, and model
- Live dashboards show spend curves against budget
- Drift detection triggers automatic model retraining
- A/B testing promotes better prediction models automatically
- Full audit trails — no prompts stored
DoCoreAI vs. Traditional APM
| Capability | Traditional APM | DoCoreAI |
|---|---|---|
| Prompt storage required | ✕ Required | ✓ Never stored |
| Autonomous budget control | ✕ Not available | ✓ Built-in |
| Intelligent pacing engine | ✕ Not available | ✓ Built-in |
| PII detection at edge | ✕ Not available | ✓ Built-in |
| Token-level cost tracking | ~ Partial | ✓ Per request |
| Multi-LLM support | ~ Partial | ✓ All major providers |
| Compliance-ready architecture | ✕ Logs prompts | ✓ Zero prompt storage |
| Zero code changes to integrate | ✕ Requires wrapping | ✓ Auto-patches SDKs |
| Self-improving prediction model | ✕ Not available | ✓ Auto-retrains on drift |
Three commands. Production-ready.
Install in the same Python environment as your application. Python 3.12+ required. Works on Windows, macOS, and Linux.
Generate your free org token at docoreai.com and run the one-time setup. Takes under two minutes.
DoCoreAI automatically intercepts all LLM SDK calls. No changes to your application code required. Ever.
DoCoreAI doesn't just monitor —
it acts autonomously on your behalf
Most observability tools watch and report. DoCoreAI watches and acts. Budget decisions, model retraining, pacing adjustments, and governance blocks all fire in real time — without human approval.
Built for every team
running AI in production
From SaaS platforms to regulated enterprises — DoCoreAI adapts to your usage patterns, compliance requirements, and budget constraints.
Budget exhausted by noon. Service down for 12 hours.
A marketing email triggers a customer surge at 9 AM. Without pacing, the entire daily budget is gone by 11 AM — leaving your AI service unavailable for the remaining 13 hours. Support tickets flood in. The engineering team scrambles.
- Budget exhausted by 11 AM
- Service down 13 hours
- 50+ support tickets
- Emergency engineering response
- No warning before it happened
- Spike detected, throttling engaged
- Service runs full 24 hours
- Same daily budget — no increase
- Zero engineering intervention
- Automatic — no human action needed
Black Friday volume 5× normal. Budget planned for average days.
Holiday traffic runs 5× your normal daily volume. Without seasonal awareness, aggressive throttling kicks in during your most critical sales window — degrading customer experience exactly when it matters most.
- Throttling during peak sales hours
- Degraded AI responses on Black Friday
- No seasonal pattern awareness
- Budget exhausted mid-campaign
- Lost conversions at peak moment
- Peak-aware strategy recognises anomaly
- Allows temporary over-pace during sale
- Compensates automatically in off-hours
- Spend stays within monthly envelope
- Full quality during critical window
Need full AI observability. Cannot log a single prompt.
Healthcare and financial services teams need complete cost visibility and governance. But traditional observability tools log prompt content — making them a compliance and legal liability before they've even been deployed.
- APM tools require prompt logging
- Compliance team blocks deployment
- Legal flags data retention risk
- AI pilot stalls indefinitely
- Flying blind on costs and performance
- Zero prompt storage — ever
- Metadata-only telemetry architecture
- PII detection blocks leaks at edge
- Full cost visibility, zero compliance risk
- 7-year audit trail — no sensitive content
AI-Powered SaaS
Keep AI features running 24/7 without budget surprises. Automatic pacing handles traffic spikes invisibly.
Financial Services
Full AI observability with zero prompt retention. SOX-compliant audit trails without compliance risk.
Healthcare
HIPAA-friendly architecture. PII detection at the edge. Patient data never leaves your network.
Retail & E-Commerce
Seasonal pattern learning keeps AI performant during peak campaigns without blowing the monthly budget.
Developer Tools
Drop-in SDK integration. Per-team cost attribution. Stop guessing which feature is burning your AI budget.
Enterprise & Government
Multi-tenant governance, RBAC, and SSO support. Built for organizations where security is non-negotiable.
17 months of research.
Real signals. Real momentum.
Built on waste patterns observed across 20+ enterprise AI engagements. Every feature exists because a real team hit a real wall.
From idea to production-ready platform
Identified the privacy vs. visibility gap across 20+ enterprise AI engagements. Started building DoCoreAI to solve it at the infrastructure level.
✓ CompleteCore observability SDK published. LLM cost, token, and latency tracking across OpenAI, Anthropic, and Groq.
✓ Live on PyPIBudget pacing, soft limits, and PII detection engine added. Multi-provider support expanded to include Google Gemini and AWS Bedrock.
✓ Live on PyPIPredictive budget model, A/B testing, drift detection, and auto-retraining shipped. Closed source from v2.0. Selected for Anthropic & AWS Agentic AI Accelerator, Bengaluru 2026.
✓ Current versionFull cloud dashboard with real-time spend curves, per-team attribution, and anomaly detection. Enterprise pilot program opens.
⏳ Coming July 2026RBAC, SSO/SAML, and multi-tenant governance for enterprise deployments. SOC2 certification targeted.
⏳ RoadmapLive on PyPI
Install today with
pip install docoreai
.
Full source available at
pypi.org/project/docoreai
.
Python 3.12+ required.
Anthropic & AWS Accelerator
Selected for the Anthropic & AWS Agentic AI Accelerator program, Bengaluru 2026 — validating the architecture and the market opportunity.
Problem validated at scale
The same privacy vs. visibility gap was observed in every single one of 20+ enterprise AI engagements. This is not a niche problem — it is the default state of production AI.
"Before DoCoreAI, I advised 20+ enterprise AI teams on production readiness and governance. The same gap appeared every time — teams could build impressive AI features but couldn't answer basic questions like 'Why did costs spike 400%?' or 'Did customer PII leak through that API call?' AI initiatives died not from technical failure but from lack of visibility and control. DoCoreAI is my answer to that problem."
Global AI observability and monitoring market by 2033, growing at 22.5% CAGR. Privacy-conscious enterprises represent approximately $3.2B of that addressable market.
Of AI-deploying organizations will implement dedicated AI observability tools by 2028 to monitor model performance, bias, and outputs — up from near zero today.
Anthropic & AWS Agentic AI Accelerator · Bengaluru 2026
DoCoreAI was selected for the Anthropic & AWS Agentic AI Accelerator program — recognising its autonomous budget management architecture as a genuine advance in production AI infrastructure.
Running LLMs in production?
Let's solve it together.
We are actively seeking 3–5 enterprise design partners for no-cost pilots with white-glove founder support. If your team is wrestling with AI cost visibility or compliance constraints — this is built for you.
- Full platform access at zero cost
- Direct founder support and configuration help
- Early access to enterprise features
- Input into the product roadmap
- Co-development of your specific use case
- Priority access when Pro tier launches
- Honest feedback on what works and what doesn't
- Real usage data to improve prediction models
- Willingness to co-develop your use case with us
- A case study if outcomes are strong
Prefer email? Reach Saji directly at saji.john@docoreai.com