Building Agentic Workflows Inside Your Own Cloud

Why Firms Are Moving AI Workflows In-House

The industry narrative around AI in legal and professional services has shifted. Eighteen months ago, the story was integration: bolt a chatbot onto Salesforce, add AI features to your document-automation vendor, let the SaaS vendor handle the intelligence layer. Today, the story is ownership. Firms that depend on vendor-hosted AI for contract review, KYC, or deal abstraction have discovered they're paying per-transaction, per-document, or per-seat for workflows that should be deterministic and repeatable. When a vendor deprecates a feature, sunsets a model, or changes their pricing tier, the firm has no recourse except renegotiation.

The move to agentic workflows inside your own tenant is not optional anymore. It's table stakes.

What We Shipped: Clearloom Agents

Clearloom Agents are custom agentic systems built on your codebase and running in your cloud account. Unlike SaaS agents that live behind a vendor's API wall, Clearloom Agents integrate directly into your data layer, your matter structures, your existing tools, and your authentication. They're not chat interfaces; they're orchestrated workflows that read documents, extract structured data, synthesize findings, and populate your downstream systems without human review loops.

Each agent is shaped to a specific workflow your firm runs repeatedly. A contract-review agent reads documents from iManage or SharePoint, abstracts key terms into a structured schema, flags risks against your firm's standard clauses, and writes a summary memo. A KYC agent ingests regulatory filings, corporate documents, and beneficial-ownership registries, then populates your intake or due-diligence checklist. A billing agent reads draft documents, reverses-engineers time entries and scope from narrative text, and syncs the output to your billing system.

The agent logic lives in your GitHub. The execution runs in your AWS, Azure, or GCP account. The data never leaves your tenant. When you need to change how the agent behaves, you modify the code, push it, and the change is live. No API-rate-limit surprises, no per-document surcharge, no waiting for a vendor feature request to be approved.

Why We Built This

Clearloom's core thesis is ownership: the code you depend on should live in your GitHub, run in your cloud, and cost you a flat fee regardless of scale. Agents are the natural extension of that thesis into AI.

Professional services firms spend $5k to $22k per month across a SaaS stack that often includes three to five AI or document-automation tools, each with its own data model, its own authentication scheme, and its own per-transaction or per-seat pricing. A firm with 100 attorneys might pay $15k/month for a contract-review SaaS, $8k/month for a document-automation vendor, $6k/month for a billing-integration tool, and $4k/month for a separate KYC platform. That's $33k/month for workflows that should live in one system.

When that same firm grows to 200 attorneys, each of those bills roughly doubles. The review agent you built in-house stays flat.

How It Works in Practice

Imagine a 60-person litigation firm with a portfolio of 40 active matters. Every matter generates discovery, motions, settlement agreements, and billing reconciliations. Today, the firm uses one SaaS vendor for contract review (charged per document), another for document automation (per-seat licensing), and manual time-entry reconciliation for billing.

With Clearloom Agents, we build a single review agent that:

• Reads new documents dropped into each matter's iManage folder
• Extracts key metadata (parties, dates, obligations, risk flags) into a normalized schema
• Compares extracted terms against the firm's standard clause library
• Writes a summary abstraction and flags edge cases for human review
• Syncs findings to the matter workspace in your existing project-management system

The agent runs on a schedule or on-demand. It costs nothing per document. When discovery volume spikes in Q3, there's no renegotiation. When you want to add a new risk-flag category, you add it to the code and redeploy.

What Changes for Firms

Before: Firm pays per-document SaaS fees, waits for vendor feature requests, has no visibility into how documents are analyzed, and can't reuse the vendor's trained model across workflows.

After: Firm owns the agent code, runs it in-house, modifies behavior on demand, and can spin up new agents (deal review, KYC, billing intelligence) without new vendor relationships.

Cost impact is typically 60-80% reduction in the SaaS line items those agents replace. Firms that were paying $15k/month for three different AI tools often move to a flat Clearloom retainer that covers unlimited agent builds and modifications.

How to Try It

Clearloom Agents are part of the Singularity platform (v6). They integrate with Atlas Workspaces (matter-scoped environments), Clearloom Lists (curated knowledge graphs), and the MCP+API layer that connects to iManage, SharePoint, OneDrive, and your other systems.

If you're running a SaaS stack with multiple AI or document-automation vendors, schedule a SaaS audit with us. We'll map your current spend, identify which workflows are candidates for custom agents, and show you the ownership and cost story. Code lives in your GitHub. No vendor risk. No per-seat ratchet.

Visit https://atlas-ai.io to learn more.