JBS Dev

Portfolio Companies Don't Wait
For Your AI POC to Finish.

We build production AI systems for private equity firms that need to move at deal speed — not another 18-month tech transformation.

SOC2
AWS Advanced Partner
HIPAA Ready

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Deployment guide + 30-min architecture call with a senior engineer.

100% confidential. We sign NDAs before technical discussions.

AI That Moves at Private Equity Speed

You don't have time for innovation theater. Between LOI and close, you need AI that surfaces red flags, validates financial models, and monitors portfolio performance in real-time. We build systems that integrate with your deal flow, not replace your process.

Senior-Led

Work directly with expert engineers, never a B-team.

High-Velocity

From discovery to production in weeks, not months.

AWS Native

Built on enterprise-grade AWS infrastructure.

80%

Faster Due Diligence Cycles

<4

Weeks to Production

100%

Firm IP Ownership

24/7

Portfolio Monitoring

The Brutal Truth

Most PE firms are stuck running diligence in spreadsheets and slide decks. We build AI systems that automate portfolio monitoring, accelerate due diligence, and help deal teams make faster decisions backed by real data.

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Private Equity Can't Wait for Generic AI Tools

Deal speed demands purpose-built intelligence

What Generic Tools Can't Do

  • close Can't integrate with proprietary deal databases or CRM systems
  • close No access to private portfolio company data or financial systems
  • close Generic models that miss sector-specific risks and patterns
  • close Slow deployment cycles that miss deal windows

What We Build for PE Firms

  • check_circle Integrated with Salesforce, Affinity, DealCloud, and portfolio ERPs
  • check_circle Real-time monitoring across portfolio company financials and operations
  • check_circle Sector-tuned models that flag PE-specific risks and opportunities
  • check_circle Deployed in weeks, not quarters — ready for your next deal
Off-the-shelf AI platforms can't access your portfolio data, understand sector-specific red flags, or integrate with deal workflows. We build AI that accelerates diligence and monitors performance without adding overhead.
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Built for Deal Speed. Proven in Production.

Due Diligence Automation: From Weeks to Days

The Challenge

Deal teams spent weeks manually reviewing financial statements, legal docs, and operational data across target companies. By the time diligence was complete, valuations had shifted or competitors had moved.

The Solution

JBS Dev built an AI pipeline that ingests financial statements, contracts, and operational metrics — then surfaces anomalies, validates EBITDA adjustments, and flags legal risks in real-time using Amazon Bedrock and custom sector models.

The Result

Due diligence cycles cut from 6 weeks to under 10 days. Deal teams identify red flags faster and make higher-conviction bids.

Portfolio Monitoring: Real-Time Performance Intelligence

The Challenge

Portfolio company performance data arrived in monthly board decks — too slow to catch operational issues, cash flow problems, or margin compression before they became crises.

The Solution

JBS Dev built an AI-powered portfolio monitoring system that integrates with portfolio company ERPs, financial systems, and operational databases — surfacing variance alerts, trend analysis, and predictive cash flow warnings in real-time.

The Result

Portfolio teams identify underperformance 4-6 weeks earlier. Faster interventions prevent value erosion and improve exit multiples.

Deal Sourcing Intelligence: From Noise to Targets

The Challenge

Deal teams wasted time chasing cold leads and reviewing hundreds of investment memos that didn't fit thesis criteria. Sourcing was manual, reactive, and missed high-quality proprietary opportunities.

The Solution

JBS Dev built an AI deal-sourcing engine that ingests public filings, news, earnings calls, and proprietary data sources — then scores companies against investment thesis, flags inflection points, and prioritizes outreach targets.

The Result

Deal teams focus on high-probability targets. Sourcing efficiency improved by 60%, and proprietary deal flow increased.

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Why Most AI Projects Fail Private Equity Firms

Generic AI vendors don't understand deal speed, portfolio complexity, or sector-specific risk patterns. They sell you a vision, hand you off to consultants who've never worked a deal, and deliver a POC that can't access your data.

Why Vendors Fail PE Firms

  • warning Can't integrate with portfolio company systems or deal databases
  • warning No sector expertise — generic models miss PE-specific patterns
  • warning Deployment cycles measured in quarters, not weeks
  • warning Black-box SaaS that creates data security and IP ownership risks

AI Built for Private Equity Reality

Every solution is architected for deal speed and portfolio complexity. We don't build AI for technology's sake — we build systems that help deal teams move faster, make better bids, and protect value in portfolio companies.
PE-Native Engineering: Our team has built AI systems for PE firms, family offices, and investment platforms. We understand deal cycles, portfolio monitoring, and sector-specific risks — not just machine learning.
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Questions Private Equity Firms Ask Us

How does JBS Dev ensure data privacy in an Agentic AI workflow?

Our architecture utilizes private VPC environments and Amazon Bedrock Guardrails to ensure your data never leaves your infrastructure or trains public models. We implement enterprise-grade encryption and PII redacting layers before any data reaches the LLM.

How quickly can a production-grade agent be deployed?

Because we focus on high-velocity engineering, we move from discovery to a functional "Sidecar" agent in weeks, not months. We prioritize integrating with your existing tech stack to avoid "from-scratch" delays.

How do you handle errors in complex tasks?

We don't rely on "black box" logic. Every JBS agent includes a Human-in-the-loop validation layer and a multi-step "Chain of Thought" verification process to eliminate hallucinations and ensure technical precision.

Can these agents work with our existing legacy systems?

Yes. We specialize in building custom connectors for Legacy SQL, Mainframes, and proprietary databases. Our goal is to make your existing data accessible to AI without a total system overhaul.

What is the ROI of Agentic AI vs. Traditional methods?

Traditional methods are limited by manual processes. JBS agents provide significant improvement in efficiency by automating the "doing," not just the "summarizing."

Who owns the IP of the custom agents built by JBS Dev?

You do. JBS Dev builds custom software on your infrastructure. Unlike "black-box" SaaS platforms, the proprietary logic, integration code, and agent architectures we deploy are fully owned by the client.

How do you prevent "Prompt Injection" attacks on enterprise agents?

We utilize Amazon Bedrock Guardrails combined with custom "Input Sanitization" layers. Every prompt is intercepted and scrubbed for malicious patterns before it ever touches the LLM inference engine.

Can these agents handle 10,000+ concurrent tasks?

Yes. By leveraging AWS Lambda and serverless orchestration, our agentic workflows scale horizontally. We don't build on single servers; we build on cloud-native architecture that expands to meet demand instantly.

How does the agent stay updated as our systems evolve?

Our agents are built with modular API connectors. If you update your database or change your CRM, we simply swap the "Action Tool" in the agent's library without having to retrain the core intelligence.

Will this agent require our senior staff to learn new languages?

No. The interface is natural language. Your experts interact with the "Sidecar" agent in plain English (or via existing dashboards) while the agent handles the complex code and data retrieval in the background.

What is the sub-second response time for agents pulling from legacy data?

We optimize latency using Vector Caching and Amazon OpenSearch. By indexing legacy metadata, the agent can "locate" the necessary record in milliseconds, ensuring the total "Thought-to-Action" cycle stays under 2 seconds.

What happens if the underlying LLM (like Claude or GPT) goes offline?

We design for LLM Redundancy. Our orchestration layer can automatically "failover" to a secondary model (e.g., from Claude 3 to Llama 3) to ensure your business-critical workflows never stop.

We'll build your proof of concept for free.

Seriously. Tell us what your last vendor couldn't deliver. We'll build a working POC in 2 weeks — you keep the code, no strings attached.

We'll reply within 4 hours with scoping questions. No sales drip.