Agentic Reliability Intelligence

Forecast production issues before they break. The only dev platform that closes the development cycle gap.

// CLIENTS

002

COSMED

BEDROCK AI

PLAYGENT AI

POLICY CORTEX

SMARTUP

CLAM

BLOCKHOUSE

COSMED

BEDROCK AI

PLAYGENT AI

POLICY CORTEX

SMARTUP

CLAM

BLOCKHOUSE

COSMED

BEDROCK AI

PLAYGENT AI

POLICY CORTEX

SMARTUP

CLAM

BLOCKHOUSE

// SECTION: WHY_OVERCAST

003

Why Overcast?

The first-of-its-kind dev platform that closes the development cycle gap. Overcast combines log curation with codebase analysis to forecast production issues before they break—no trigger event needed.

Forecast what existing code will break production

Overcast curates your logs itself (or through integrations) and reads your entire codebase to come up with a forecast of what existing code will break production using dev code and logs as evidence. By combining log curation with codebase analysis, we identify patterns and potential production failures before they occur.

No trigger event needed—real-time production code suggestions as new features are pushed. Overcast analyzes your codebase in depth, automatically generates PRs to fix forecasted issues, and provides the code changes needed to prevent production breaks before they happen.

forecast.jsexample

$ overcast forecast

│

├─ curate logs + analyze codebase

│ └─> identify patterns

│

├─ forecast failures

│ └─> generate PR fixes

│

└─ prevent breaks

// Example: Combined analysis

const forecast = await overcast.forecast({

logs: curatedLogs,

codebase: fullCodebase,

patterns: identifiedPatterns

});

// Returns: { risk: 0.85, fix: PR#1234 }

// SECTION: THE_GAP

005

How Overcast Differs

While others focus on reactive monitoring or code review alone, Overcast combines both approaches to forecast production issues before they break—closing the development cycle gap.

Post-Production Monitoring

DatadogSentry

WHAT_THEY_DO

Curate logs, error handling after trigger events

LIMITATION

Reactive - only helps after issues occur

CAPABILITIES

Log curation

Error handling

After trigger events

Forecast issues

Codebase analysis

Proactive prevention

AI Code Review

Code RabbitGreptile

WHAT_THEY_DO

AI code review and PR suggestions

LIMITATION

Only analyzes code, not production behavior

CAPABILITIES

Code review

PR suggestions

Code analysis

Log integration

Production forecasting

Real-time suggestions

Overcast - The Gap Closer

Overcast

WHAT_WE_DO

Combines both approaches

OUR EDGE

Forecasts production issues before they break

VALUE

Closes the development cycle gap

CAPABILITIES

Log curation

Codebase analysis

Production forecasting

Real-time suggestions

No trigger event needed

Automated PR fixes

UNDER_THE_HOOD

Under The Agentic Hood

How our agents are RL-Equipped: advanced RL algorithms that forecast and advise, heatmapping areas of code that are problematic, chain-of-dependency chunking while you push from dev to prod.

rl_agent.pyrl algorithms

▸PPO approaching entropy collapse at ep ~480. Recommend increasing ent_coef to 0.02 and clipping reward signal — projected +12% stability.

ppoa3cdqnbaseline800 episodes

01 // RL_ALGORITHMS

Advanced RL Algorithms

Our agents use reinforcement learning algorithms that continuously learn from production patterns, forecasting issues and providing actionable advice before problems occur.

These RL algorithms are sourced from the same battle-tested systems used at quantitative trading firms for high-frequency trading, where millisecond-level decision-making and pattern recognition are critical. This pedigree ensures our forecasting and advisory capabilities operate at the highest level of precision, adapting to your production environment's unique patterns with the same rigor applied to financial markets.

auth_handler.pyheatmap

01import hashlib, secrets, time

02from db import users, sessions

04def hash_password(pw: str) -> str:▪ 18

05 return hashlib.sha256(pw.encode()).hexdigest()▪ 18

07def authenticate(user, pw):▪ 74

08 record = users.find_one({"email"}: user})▪ 51

09 if record["pw"] == hash_password(pw):▪ 91

10 token = secrets.token_hex(16)▪ 88

11 sessions[token] = {"user": user, "ts": time.time()}▪ 92

12 return token▪ 67

13 return None▪ 44

15def validate_session(token: str) -> bool:▪ 55

16 s = sessions.get(token)▪ 53

17 if not s or time.time() - s["ts"] > 3600:▪ 71

18 del sessions[token]▪ 49

19 return False▪ 22

20 return True▪ 15

lowmedhighcritical4 critical lines

02 // CODE_HEATMAP

Problematic Code Heatmapping

Intelligent heatmapping identifies areas of code that are most likely to cause production issues, allowing teams to focus on high-risk areas before deployment.

By analyzing historical failure patterns, code complexity metrics, and dependency relationships, the heatmap provides a visual risk assessment that prioritizes critical code paths. This enables teams to allocate review resources effectively, catch potential issues early, and deploy with greater confidence knowing that high-risk areas have been thoroughly examined and validated.

chunker.pydep chunking

21−def chunk_modules(graph, max_size):

22− chunks, cur, size = [], [], 0

23− for node in graph.nodes:

24− size += graph.nodes[node]["size"]

25− cur.append(node)

26− if size > max_size:

27− chunks.append(cur)

28− cur, size = [], 0

29− return chunks + [cur]

21+def chunk_modules(graph, max_size):

22+ # toposort keeps deps before dependents

23+ ordered = nx.topological_sort(graph)

24+ chunks, cur, size = [], [], 0

25+ for node in ordered:

26+ n = graph.nodes[node].get("size", 1)

27+ if size + n > max_size and cur:

28+ chunks.append(cur); cur, size = [], 0

29+ cur.append(node); size += n

30+ return chunks + ([cur] if cur else [])

before

after

+10 / −9

03 // DEPENDENCY_CHUNKING

Chain-of-Dependency Chunking

As you push from dev to prod, our agents perform chain-of-dependency chunking, analyzing how changes propagate through your system and identifying potential failure points.

This allows for rapid code deployment with confidence, as the system maintains full observability across dependency chains even as state changes propagate through your infrastructure. By understanding the complete dependency graph before deployment, teams can push code fast while maintaining visibility into how each change impacts downstream services and state transitions.

// SECTION: SOLUTIONS

004

Production Forecasting

Forecast what existing code will break production using dev code and logs as evidence. Real-time production code suggestions as new features are pushed—no trigger event needed.

Distributed Tracing

Automatically correlates logs from multiple agents and systems into a single, coherent timeline for forecasting how failures will propagate.

GitHub Ingestions

Reads your codebase and identifies which code changes will cause production issues with git blame, PR tracking, and stack trace analysis for forecasting.

Domain Specific Intelligence

Our agents are designed for your specific regulations & rules, with customizable Prompt Packets that fit your needs. Whether you're in healthcare, finance, or any regulated industry, agents understand your unique constraints and requirements.

Lightning Fast Analysis

8-15 seconds from log ingestion to complete forecasting analysis. Intelligent filtering reduces LLM costs by 80%.

AI That Forecasts Production Issues

Complete production forecasting with evidence, timeline reconstruction, and confidence scoring. Not just anomaly detection—real predictions before issues occur.

Automated PR Fixes

Automatically generates PRs to fix forecasted issues, providing the code changes needed to prevent production breaks before they happen.

Enterprise-Ready Security

Multi-tenant architecture with complete customer isolation. Row-level security ensures your data stays yours.

MCP_COMMANDS

With Overcast MCP, you control your incidents

We fill in the gaps so teams can ship safely. Overcast commands are fast, quick, and to the point. A layer on top of your observability with MCP agents - tracing performance, limiting risky actions, and turning noisy signals into decisive fixes.

list_incidents

root_cause

analyze_logs

deploy_diff

assign_owner

link_ticket

runbook_apply

safe_rollback

silence_alerts

guardrails_verify

sim_incident

auto_fix

status_board

trace_perf

agent_mesh

resume_tl

SDK_SETUP

Set Up in Minutes

Get started with Overcast in minutes. Our drag and drop SDK makes setup effortless—no complex configuration or infrastructure changes required.

Drag & Drop Setup

Easy drag and drop SDK for instant setup. No complex configuration needed.

Automatic Integration

SDK automatically connects to your codebase and log sources for seamless forecasting.

Start Forecasting

Begin forecasting production issues immediately with real-time code suggestions.

SDK_INTEGRATIONnpm install @overcast/sdk

sdk.jsintegration

import { Overcast } from '@overcast/sdk'
// Drag & drop setup
const overcast = new Overcast({
  apiKey: process.env.OVERCAST_API_KEY,
  // Auto-detects codebase
  // Auto-connects to logs
})
// Start forecasting
overcast.forecast()
// Real-time suggestions
overcast.on('suggestion', (fix) => {
  // Auto-generates PR
  console.log(fix)
})

FEATURES

• Auto-detects codebase structure
• Connects to existing log sources
• Zero configuration required
• Real-time forecasting enabled

// SECTION: ABOUT_OVERCAST

005

MTTR_REDUCTION50%

ISSUES_RESOLVED8

RESOLUTION_TIME30min

UPTIME99.8%

TESTIMONIALS.mdv3.1.0

Cosmed

See how Overcast helped this Medical Manufacturing Company cut MTTR in half

Nathan Miller

Product Manager

Cosmed, a leading medical manufacturing company, faced significant challenges with distributed tracing across their microservices architecture. The engineering team had successfully resolved seven production issues, but an eighth issue remained undiagnosed for over a week despite extensive debugging efforts. Traditional monitoring tools failed to correlate logs effectively across their complex system architecture. After implementing Overcast, the platform analyzed the codebase and correlated logs from multiple systems, identifying the root cause—a subtle race condition that only manifested under specific load conditions—in less than 30 minutes. Overcast's forecasting capabilities have reduced Cosmed's mean time to resolution by 50%, enabling the engineering team to shift focus from firefighting production incidents to building new features and improving system reliability.

// SECTION: PRICING_TIERS

006

Pricing

Choose the plan that fits your team. Scale as you grow.

live throughput: 0.0k req/s

DEVS_INDIES

$0/user/mo

Plug-and-play sidecar for instant reliability

Overcast SDK — drag & drop setup

Production forecasting + auto PR fixes

3 agents

7-day history

Community support

GET STARTED

STARTUPS_SMB

RECOMMENDED02

$00/user/mo

Compliance-ready audit trails + secure logging

Advanced forecasting + multi-agent analysis

Automated PR fixes + postmortems into Jira/Confluence

10 agents

30-day history

99.9% SLA

GET STARTED

ENTERPRISE

CUSTOM

Unlimited agents + custom integrations

HIPAA/FedRAMP compliance + on-prem deployment

Custom 20B parameter model for local running

Production forecasting + predictive analytics

Unlimited agents

1-year+ history

24/7 support · 99.99% SLA

TALK TO US