Integration
Connect the environment you already run.
Bring permissioned data, documents, identity, and workflow tools into one operating context—without replacing the systems teams depend on.
eSapiens · Vertical AI software · Est. 2024
Built for the depth
of real industries.
eSapiens builds AI-native software for markets where domain expertise, data integrity, and trusted execution matter.
Distinct products for distinct professional workflows — built with a shared discipline around context, evidence, control, and outcome.
Specialized products. Shared principles. Accountable outcomes.Database intelligence · Investment banking · Private markets · Private creditSpecialized products. Shared principles. Accountable outcomes.Database intelligence · Investment banking · Private markets · Private creditSpecialized products. Shared principles. Accountable outcomes.Database intelligence · Investment banking · Private markets · Private creditSpecialized products. Shared principles. Accountable outcomes.Database intelligence · Investment banking · Private markets · Private credit
01 Our portfolio
A growing portfolio,built from the market inward.
Each eSapiens product begins with a specific market, a consequential workflow, and the people accountable for getting it right. The result is not one generic agent stretched across industries — it is software shaped to the language, data, rules, and outputs of the work itself.
ThunderScan
Diagnose database health, structural risk, and AI readiness before the work begins.
Thor
Ask databases in plain English, with self-checking intelligence before teams act.
Derek
Run VC and PE portfolios, reporting, and investor operations in one system.
Magnum
Draft deal documents, control models, and run diligence on one governed record.
Lucia
Unify underwriting, portfolio monitoring, and private credit operations.
The portfolio is designed to grow. New products enter when eSapiens can bring genuine technical and market depth — not simply another AI interface. Talk to us about a market →
02 A shared discipline
From complex contextto accountable action.
The data and output change from market to market. The discipline does not. Every eSapiens product carries the same six-step operating pattern — from the messy reality of a domain to a result that can be checked, reviewed, and used.
Step 01
Connect the context
Bring together the authorized data, documents, models, policies, and records around the work.
Step 02
Structure the work
Identify the entities, relationships, stages, requirements, and missing information.
Step 03
Reason within the domain
Apply market language, business logic, permissions, and workflow constraints.
Step 04
Check the evidence
Link the output back to the SQL, source material, rule, or calculation that supports it.
Step 05
Keep people in control
Make review, approval, correction, and override visible parts of the workflow.
Step 06
Move work forward
Deliver an analysis, dashboard, report, decision package, or operational action that can be used next.
03Our approach
We don't add AI to software.We rebuild the workflow.
The strongest AI products begin with the operating reality of a market: what information matters, who can access it, how judgment is made, what must be reviewed, and what a useful outcome looks like.
Four principles
Working principle
Operating model
The domain becomes the product model.
Private credit workspaceModel ready
Language
Data model
Rules
Exceptions
Underwriting workflow
Entities, covenants, diligence stages, and permissions are structured into one operating context.
Portfolio companyFacilityCovenant
Finished work
The answer is the document, not the chat.
Quarterly review · Atlas Fund IIPreparing3 deliverables ready
Request
Prepare the Q3 covenant review for Thursday’s credit committee.
DOCX
Covenant review memo14 pages · house template
Ready
XLSX
Compliance schedule3 tabs · 12 facilities
Ready
PDF
Committee summary2 pages · for signature
Ready
Decision control
AI prepares. A person decides.
Credit committee · Deal 14Awaiting reviewer
AI proposal
Approve with one condition: attach the updated liquidity covenant before funding.
3 rules checked5 sources linked1 exception open
Reviewer decision
Send backwith guidance
Approve with conditionrelease once attached
Overriderecord the reason
Escalateto full committee
AM
Avery MorganAccountable reviewer · signed 14:02
Recorded
Data boundary
One client. One workspace. One set of rules.
Workspace · Atlas CapitalBoundary enforced
Inside your boundary
Data roomRead · 12 users
WarehouseRead · 4 users
Policy binderRead · all
Dedicated, isolated workspaceNo shared index across clients
Source permissions mirroredNobody sees more than before
Never used to train modelsOurs or anyone else’s
Export in full, any timeDocuments, records and history
04Enterprise ready
Built to enter the enterprise without disrupting it.
Four practical requirements shape every deployment: integration with the systems already in place, a legible technical stack, safety inside the workflow, and support beyond launch.
Technical stack
A stack technical teams can inspect.
API-first, model-flexible, and observable by design. Context, tools, models, and actions remain legible at every layer.
Safety
Controls live inside the workflow.
Role-based access, source trails, review gates, and auditable actions keep sensitive work governed from question to outcome.
Support
A deployment partner after launch.
Hands-on implementation, team enablement, monitoring, and workflow guidance keep the system improving in real operations.
05Case studies
Trusted where operational answers have to be right.
Customer evidence matters more than broad claims. These stories show how domain-specific AI changes the work around real decisions.
Thor · Customer story
SagacityGolf operations
Operational answers
Less technical dependency
Reliable query skills
Thor helps our business executives with answers to operational questions without the need for our technical staff to assist them on every request. Its ability to add skills to correct or simplify queries is key to making the tool reliable and productive.
Duane CrawCTO, Sagacity Golf
Lucia · Customer story
Golden Section Venture CapitalPrivate credit
Credit memos in minutes
Covenants watched automatically
Revenue-based billing
Before Lucia, every new deal meant re-uploading the data room and rebuilding the same spreadsheet. Now the screen is done by the time we open the memo, covenants are checked against what the borrower actually reported, and the monthly statements go out without anyone touching Excel. It gave us back the week we used to spend on paperwork.
Will McCallumDirector, Golden Section Venture Capital
06 Blog
Thinking from the edgeof AI and industry.
Research, field notes, technical guides, and product papers on AI-native software, data architecture, market structure, and the systems required to make intelligent work trustworthy.
Browse all posts
New wine meets old wineskins — creative destruction comes for SaaS.
Nobel-winning growth theory, the $300B February repricing, and what Intercom and Salesforce learned about pouring AI into legacy architecture.
Thought leadership · Market structure22 min →
AI-native B2B application development — from tools to systems of action.
Why building AI into professional work means rethinking data, permissions, and review — not adding a chat window to legacy software.
Thought leadership · Vertical AI28 min →
From SaaS to AIaaS — how to survive the SaaS-pocalypse.
Why the interface is being commoditized, what agentic intelligence as a service actually sells, and a three-step path to embedding agents in software you already run.
Thought leadership · Product building6 min →
Database schema design for AI-ready systems.
The structural preconditions that decide how far intelligent workflows can safely go on top of a production database.
Field guide · Data architecture14 min →
THOR — let your data do the talking.
A business-level walkthrough of the THOR paper: a five-step pipeline that turns plain-English questions into verified, read-only SQL.
Research · Applied AI12 min →
Database normalization and schema design.
Why a properly normalized schema is the load-bearing wall of every application — the normal forms, the anomalies they prevent, and seven common pitfalls.
Field guide · Data architecture21 min →