Kolas Technologies

Build AI that learns with your organization.

Kolas builds the infrastructure that makes it possible — a secure knowledge layer across the systems you already use, AI workflows that run on your organization’s full context, and feedback loops that make every output better than the last.

Built from real workflows across investment management, wealth management, accounting, and commercial real estate

Today

Your organization’s knowledge is scattered.

Your organization already has the knowledge that should make AI useful: memos, reports, client files, CRM notes, meeting transcripts, email threads, and years of decisions. The problem isn’t collecting more information.

The problem is that this knowledge is fragmented and siloed — difficult to retrieve, difficult to trust, and impossible for AI to reason over consistently.

None of this gets fixed by another standalone tool. It gets fixed with infrastructure.

Memos Reports CRM notes Transcripts Client files Diligence files SharePoint Drive Slack Email Internal databases Team memory

What that looks like day to day:

  • The same questions get answered again and again
  • Context is rebuilt from scratch for every deal, client, and report
  • Previous work is forgotten — or lives in one person’s head
  • Documentation is stale the day it’s written
  • AI tools answer generically, with none of your organization’s context
The Foundation

Your systems stay. Kolas connects them.

One secure intelligence layer across everything your organization already knows.

Kolas connects SharePoint, Drive, Slack, email, CRM, and internal databases into a permission-aware, source-cited knowledge layer — then puts workflows, feedback, and people on top. Nothing migrates. Everything becomes usable.

01

Existing Systems

SharePoint / OneNote Google Drive Slack / Teams Email CRM / DealCloud Documents & Decks Meeting Transcripts Internal Databases
02

Kolas Knowledge Layer

Ingestion & Extraction Structure Permissions Search Retrieval Citations Audit Logs
03

Shared Learning

Analyst Edits Partner Feedback Corrections Preferences Institutional Memory
04

Agents & Workflows

Research Diligence Reporting Portfolio Monitoring CRM Enrichment Knowledge Search Operations
05

People

Analysts Partners Operations Teams Leadership

Edits and feedback flow back into the knowledge layer — every workflow makes the next one smarter.

Continuous Learning

Knowledge that compounds.

Most AI starts from zero every time. Kolas is built so your organization’s intelligence improves with use.

Knowledge

The organization’s context — structured, permissioned, cited

Workflow

An agent drafts a memo, report, or answer from it

Human feedback

Analysts edit. Partners correct. Decisions land.

Better knowledge

The correction is captured instead of lost

Future workflows start smarter

Every workflow creates new knowledge. Human corrections improve future outputs. And because every workflow runs on the same foundation, every workflow improves every other workflow.

Where this is honest today: we are not claiming AI magically learns everything on its own. We are building the infrastructure that makes compounding possible — feedback captured, knowledge structured, workflows that reuse both. The long-term direction is higher-order decision support, earned one workflow at a time.

The Difference

What changes after deploying Kolas?

Instead of generic AI…

Your AI understands your organization.

Instead of rebuilding context…

Knowledge compounds.

Instead of every workflow existing independently…

Every workflow improves the next.

Instead of institutional knowledge living inside employees…

Knowledge becomes organizational.

Instead of static documentation…

Your knowledge continuously evolves.

Instead of AI forgetting…

The organization continuously learns.

Applications

What the foundation makes possible

Agents are applications built on the knowledge layer — permission-aware, source-cited, and shaped by your firm’s feedback. These are the ones we deploy most for investment teams, our deepest vertical today.

Deal Memory Agent

Ask “have we seen this before?” and retrieve prior companies, memos, partner context, past risks, and source documents.

Thesis-Aware Deck Triage

Screen inbound decks against firm mandates and internal theses. Surface fit, strengths, risks, missing data, similar companies, and diligence questions.

Sourcing Agent

Scan approved sources, CRM records, conference lists, founder databases, publications, news, and market maps for companies that match your theses — each recommendation explains why it matters and cites the source.

Memo Support Agent

Draft IC memo sections, company briefs, and deal summaries from the firm’s own documents, with every material claim traced to the source.

Portfolio Monitoring Agent

Batch-read portfolio updates and flag missed milestones, trial delays, burn changes, hiring updates, regulatory movement, and items needing partner attention.

LP Reporting Agent

Generate draft quarterly LP updates from portfolio updates, internal notes, approved performance data, and previous reports. Flags missing inputs and cites the underlying source material.

CRM Enrichment Agent

Clean and enrich CRM records with sector, stage, geography, indication, last-interaction summaries, missing fields, stale records, and suggested follow-ups.

Conference Prep Agent

Turn attendee lists into prioritized meeting targets using CRM history, theses, prior interactions, and internal context.

Use Cases

Built for the investment lifecycle.

Sourcing and market mapping
Inbound deck triage
Diligence and IC memo support
Deal memory search
Portfolio monitoring
LP reporting
CRM enrichment and hygiene
Internal knowledge search
Proof

Built from real operating workflows.

Wealth & Investment Management
~30 hrs
saved per week across analyst workflows

Research automation for a wealth management firm — spanning research, enrichment, and lead-generation workflows.

Commercial Real Estate
1,000s
of property datapoints aggregated

Pilot with a Fortune 500 real estate brokerage — aggregating commercial property data and reducing manual research work.

Venture Capital
Pilot → platform
knowledge infrastructure and AI workflows

Building knowledge infrastructure and agent workflows for a venture capital investment team.

Selected engagements. References available on request.

Security

Designed for sensitive firm data.

The answer is not uploading everything into generic AI tools. Kolas designs systems where your firm controls where data lives, who can access it, and what each agent can see.

  • Permission-aware retrieval
  • Source-cited outputs
  • Audit logs
  • Role and team-based access
  • Redaction and exclusion of sensitive documents
  • Azure, AWS, private cloud, VPC, or hybrid deployments
  • Human-in-the-loop workflows
  • Model providers do not train on firm data unless explicitly approved
  • Data residency and cross-border access controls when needed
How We Work

From pilot to platform.

01

Map the knowledge

Identify where the important data lives and which workflow matters most.

02

Build the foundation

Create a controlled knowledge layer using a safe dataset.

03

Deploy the first agent

Launch one high-value workflow such as deal memory search, deck triage, or reporting.

04

Expand

Add new workflows on the same foundation — each one starts with everything the organization has already learned.

Start small. A focused 2–4 week proof of concept using a safe dataset and one agent. If useful, expand into an 8–12 week implementation.

Scope a Pilot
About

Infrastructure first.

Kolas builds the infrastructure that lets organizations continuously improve how AI works for them. Today that’s primarily investment firms, wealth management, accounting firms, operations teams, and commercial real estate — but the vision is broader than any one vertical.

Consulting is how organizations adopt the platform: we implement alongside your team, on your systems, starting with one workflow. The long-term vision is shared organizational intelligence — an organization whose AI is more useful every month than the month before.

Build an organization that learns.

If your team is experimenting with AI but fighting scattered context, siloed systems, and untrusted outputs, start with the foundation.

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