Growing Portfolio Companies with Generative AI
Protect your market position and drive growth at portfolio companies without the complexity of building in-house Generative AI capabilities.
The applications seem endless, but the core functionality of Generative AI has changed little since ChatGPT first arrived. The vast majority of tools — across every industry — are built on the same handful of capabilities.
Data Extraction
Transforming unstructured data, documents, or images into structured data
Data Classification
Labeling, scoring, or ranking text, images, or entities against any taxonomy or rubric
Information Retrieval
Querying knowledge bases with cited, grounded answers — typically through RAG
Text Generation
Generating, summarizing, or translating text from prompts and context
Image / Video Generation
Creating or editing visual content from descriptions
Code Generation
Writing, debugging, migrating, and explaining code
Common workflows
Document Intelligence
Parsing contracts, CIMs, board packs. Summarizing reports and earnings calls. Structured extraction from any document.
Extraction + Summarization
Enrichment & Scoring
Lead enrichment from websites. Deal scoring against criteria. Candidate ranking. Any enrich-then-evaluate pipeline.
Extraction + Scoring
Research & Retrieval
Support agents. Legal or medical research. Any knowledge base with cited, grounded answers.
Retrieval (RAG)
Content & Communication
Email personalization. Compliance screening. Drafting reports or presentations from structured data.
Generation + Classification
Code & Development
Code review, framework migration, automated documentation. Debugging. Test generation.
Code Generation
The single company
You're the CEO of a marketing software company. Your head of sales frantically runs into your room in a panic about how your top 3 competitors have implemented a Generative AI functionality that provides an enrichment feature to automatically pull key company information from a company's home page.
You've lost a couple of customers, and the world seems to be ending (at least according to your head of sales).
Your options: acquire a company that offers this functionality, or develop it in-house.
You take a look at the market and notice people want 50x multiples for their OpenAI wrapper. Your development background finds this unreasonable because the functionality is little more than scraping a website URL, generating a prompt to extract structured data, and saving it in a database.
It should be easy, right?
You run to your development team only to remember that:
• Nobody has worked with Generative AI and nobody wants to learn
• Your code is a monolith where every change has to go through a strict development process
• There is a drought of Generative AI talent on the market
• You haven't budgeted for an in-house team
The holding company dilemma
Let's change the narrative a bit. You're now the Managing Partner of a vertical software holding company with 20 companies across various verticals. You've got 10 accounting software companies and 10 legal software companies.
You decide to hold a conference for your companies to brainstorm Generative AI solutions that could protect your market position and grow your companies. The event is a success with a myriad of solutions identified by your portfolio companies.
Your companies want to develop
• A customer support chatbot for all of your companies
• A tool to help with legal research and extract key clauses from legal contracts
• A tool to parse invoices and classify income statement items
But are you really going to
• Build 20 chatbots?
• Build 10 legal data extraction and research tools?
• Build 10 accounting data extraction and classification tools?
You could. But then you'd be in the same boat as the simple scenario, except ×10. Doesn't sound like a good time. Especially because you need to show an ROI on each development. How many development hours will be wasted just on learning the basics?
Meanwhile, time keeps moving
At this point, you've likely spent a few months on these Generative AI solutions. It's been getting difficult to keep track of everything and while this has been going on:
• New models have launched that are better at specific tasks
• New features have been shipped that simplify various functionalities
• Your competitors have shipped even more features because they're microservices and you're monolithic
Build the infrastructure once, deploy it everywhere
Why not break down the products into their core functionality and build the same reusable infrastructure for every product?
Without shared infrastructure
20 separate chatbots. 10 separate extraction tools. 10 separate classification tools. Each built from scratch.
With a shared AI framework
Chatbot
×20
Extraction
×20
Retrieval
×20
Classification
×20
4 capabilities, each deployed and configured per company. Same framework, same guardrails, same infrastructure.
1 chatbot
Copied and adjusted 20 times
1 extraction tool
Copied and adjusted 20 times
1 retrieval tool
Copied and adjusted 10 times
1 classification tool
Copied and adjusted 10 times
This is what Habitat's AI framework does
The scenarios above describe the exact problem Habitat's extensible AI framework solves. A shared pipeline — context assembly, prompt execution, guardrail validation, audit logging — deployed per tenant with per-company configuration.
Extensible framework
New AI capabilities inherit the full pipeline — guardrails, streaming, telemetry, security. No rebuilding from scratch per company.
Multi-tenant isolation
Each portfolio company operates in an isolated cell with its own data, its own model configuration, and its own security context.
Fine-tuned models
Models trained on one company's data can be deployed specifically to that company's pipeline stage. The framework resolves which model to use per tenant.
Governed by default
Every AI action is audited. Agents see only authorized data. The same security model that governs human users governs AI. No ungoverned subscriptions.
Infrastructure managed
We handle scaling, model selection, prompt engineering, and staying current with the landscape. Your companies call an endpoint and get structured output.
No revenue sharing
The infrastructure is yours. No royalties, no per-query fees to a third party, no vendor lock-in on the AI layer.
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Sourcing
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StrategyDeploy AI across your portfolio
Tell us about your portfolio companies. We'll show you what shared AI infrastructure looks like for your operations.