Build agents that own ongoing work.

Build and run agents that pursue goals over time, coordinate with people, and improve with expert feedback. Built around your firm's systems, standards, and controls.

S

Welcome back, Roland

Conversations
116
Costs
$189.67
Deployments
2
Needs attentionView all
Data room sync misses re-uploaded files New

The agent read the first data room sync and missed 14 files the seller re-uploaded, citing superseded financials.

Agent struggle18 conversationsLast seen 24 min ago
Financial summary mixes fiscal and calendar years

The agent put FY and calendar-year revenue in one table, then corrected it only after the associate flagged the gap.

Agent struggle9 conversationsLast seen 3 hr ago
Datasite searches omit the project ID

The agent searched Datasite without a project ID, causing validation errors before recovering it from the deal folder.

Agent struggle6 conversationsLast seen 1 day ago
Runtimes
cim-agent
42 conversations$18.422% tool error rate

The work, done to your firm's standard.

Each agent works in your firm's templates and house style, and cites every figure and finding to its source, so your team reviews the work instead of redoing it.

Recipes

Methods, written down as agents.

Each agent is a recipe: your firm's method written down, with the skills and tools it uses and the standards it is judged by. Your engineers can read it, change it and own it.

From data room to first-draft CIM: read every document, build the financial summary and the equity story in your house style, and cite each claim to the page for the deal team's review.

Covenants, baskets and EBITDA definitions compared across every agreement in the book, each term cited to its section and every deviation from your standard terms flagged for the deal team.

Clause by clause across the data room: change of control, assignment, exclusivity and termination in one grid, every finding cited to the page, then the first draft of the diligence report.

Data Room to CIM

From data room to first-draft CIM: read every document, build the financial summary and the equity story in your house style, and cite each claim to the page for the deal team's review.

.introspection
cim-agent.yaml
.pi
mcp.local.example.json
agents
agent.yamlM
data-room-reader.yaml
financials.yaml
cim-drafter.yaml
skills
house-style
mnpi-handling
judges
citation_accuracy.yaml
house_style.yaml
evals
carve-out-sale.yaml
incomplete-data-room.yaml
SYSTEM.md
package.json
1name: agent
2description: Sell-side co-worker, data room to first-draft CIM.
3ai:
4 model: anthropic/claude-opus-4-8
5 thinking_level: high
6tools:
7 - read
8 - bash
9 - write
10mcp:
11 datasite:
12 include: ["*"]
13 sharepoint:
14 include: ["*"]
15 capiq:
16 include: ["*"]
17subagents:
18 - data-room-reader
19 - financials
20 - cim-drafter
21skills:
22 - house-style
23 - mnpi-handling
24system_instructions:
25 mode: append
26 content: |
27 # Role: Sell-side associate
28 Read the whole data room, build the financial
29 summary, and draft the CIM in the house style.
30 Cite every figure to its page; leave a gap
31 marked for the deal team rather than guess.
Credit Agreement Review

Covenants, baskets and EBITDA definitions compared across every agreement in the book, each term cited to its section and every deviation from your standard terms flagged for the deal team.

.introspection
credit-agent.yaml
.pi
mcp.local.example.json
agents
agent.yamlM
agreement-parser.yaml
covenant-extractor.yaml
comparison-grid.yaml
skills
leveraged-finance-terms
credit-memo-format
judges
definition_fidelity.yaml
section_citation.yaml
evals
amended-and-restated.yaml
covenant-lite.yaml
SYSTEM.md
package.json
1name: agent
2description: Credit co-worker, agreements in, comparison grids out.
3ai:
4 model: openai/gpt-5.5
5 thinking_level: high
6tools:
7 - read
8 - bash
9 - write
10mcp:
11 imanage:
12 include: ["*"]
13 sharepoint:
14 include: ["*"]
15subagents:
16 - agreement-parser
17 - covenant-extractor
18 - comparison-grid
19skills:
20 - leveraged-finance-terms
21 - credit-memo-format
22system_instructions:
23 mode: append
24 content: |
25 # Role: Credit analyst
26 Extract covenants, baskets and EBITDA
27 definitions from each agreement, compare them
28 to the fund's standard terms, and cite every
29 term to its section. Flag, never interpret away.
Due Diligence Review

Clause by clause across the data room: change of control, assignment, exclusivity and termination in one grid, every finding cited to the page, then the first draft of the diligence report.

.introspection
diligence-agent.yaml
.pi
mcp.local.example.json
agents
agent.yamlM
document-triage.yaml
clause-review.yaml
report-drafter.yaml
skills
firm-playbook
privilege-handling
judges
clause_recall.yaml
citation_accuracy.yaml
evals
buried-anti-assignment.yaml
scanned-contracts.yaml
SYSTEM.md
package.json
1name: agent
2description: Diligence co-worker, data room to diligence report.
3ai:
4 model: anthropic/claude-opus-4-8
5 thinking_level: high
6tools:
7 - read
8 - bash
9 - write
10mcp:
11 imanage:
12 include: ["*"]
13 intralinks:
14 include: ["*"]
15subagents:
16 - document-triage
17 - clause-review
18 - report-drafter
19skills:
20 - firm-playbook
21 - privilege-handling
22system_instructions:
23 mode: append
24 content: |
25 # Role: Diligence associate
26 Review every contract against the firm's
27 playbook, record each finding in the grid with
28 its page, and draft the report for the partner.
29 Nothing reaches the client without their read.

Runtime

Engagements, carried for weeks.

Runtime carries each engagement from first signal to final draft, for as many weeks as it takes. It reacts as filings, replies and date changes arrive, schedules the next step, waits on what others owe, and records every action so a partner can replay what it did and why.

Its own identity.

Every agent acts under an identity scoped to its work, never a person's login.

Your systems, connected.

Data rooms, document systems and market data, with only the access each agent needs.

Weeks on one engagement.

Work carries on through interruptions and restarts without losing its place.

Remembers how you work.

House style, past corrections and each engagement's history, from one session to the next.

Operator

An AI researcher inside the firm.

Operator studies where your agents fall short, learns from every expert correction, and tests proposed fixes against your firm's standards. Your team reviews the evidence and approves changes before release, so improvements remain under your control.

Live agent
Fix A
Fix B▲ +9.2pt

Build with your team. Or with ours.

Your engineers can build directly on the Platform, or work with us to deliver the first agent. Your specialists define success and review results; IT governs access and deployment. You can adopt Platform independently of Ark.

Embedded

We work alongside your specialists and engineers to define success, connect your systems, and deliver a production outcome.

Platform

Your engineers build and operate recipes on Introspection, with Operator helping them improve agents from production evidence.

Private by default, owned by the firm.

Your data stays in your cloud and never trains anyone else's model. Every agent works under your firm's identity, records and approvals.

  • Your cloud or on-prem.

    Deploy the whole stack in your own cloud or on your own servers, with keys you hold. Your data never leaves.

  • No passwords in the agent.

    Each agent gets only the access its work needs. Credentials are swapped in at the gateway, so the agent never sees one.

  • Every action on the record.

    Conversations, tool calls, costs and approvals, by agent, team and client.

  • Approval before it leaves.

    Nothing client-facing goes out without a person's yes.

What work should your agents own?