Finance ops · Production

OffDeal powers every stage of M&A advisory with one Claude-based agent

The problem

OffDeal ran more than 12 separate agentic workflows that broke whenever SOPs changed, and hit a hard ceiling when deal context exceeded the million-token limit of their prior provider, causing API calls to fail outright.

First attempt

OffDeal had been using a long-context model from another provider, packing the full context into each call to preserve accuracy over RAG, but that approach failed when context exceeded the million-token limit.

Workflow diagram · grounded in source
1
Deal phase task initiated
Trigger
A deal phase task — originating new clients, finding buyers, preparing materials, or running diligence — kicks off the workflow.
▾ source quote
“OffDeal uses AI across virtually every stage of a deal, from originating new clients to finding buyers, preparing materials, and running diligence”
2
Archie applies modular skills
Ai action
Archie combines modular self-contained skill instructions to handle the task flexibly.
▾ source quote
“the team now builds modular capabilities they call "skills," self-contained instructions that Archie can combine and apply flexibly”
3
Autonomous buyer sourcing
Ai action
A long-running agent handles buyer sourcing autonomously for up to 4 hours, researching across all 10 methods, cross-referencing findings, and recursively refining its search.
▾ source quote
“A long-running agent now handles the work autonomously for up to 4 hours, researching buyers across all 10 methods, cross-referencing findings, and recursively refining its search”
4
Personalized buyer emails drafted
Output
Archie drafts personalized chaser emails to buyers from within the firm's Deal Portal.
▾ source quote
“Archie, OffDeal's Claude-powered agent, drafts personalized chaser emails to 34 buyers from within the firm's Deal Portal”
5
Presentation decks generated
Output
Archie generates each presentation deck in about an hour.
▾ source quote
“Each deck takes about an hour with Archie, compared to the 30 to 40 hours a traditional team would spend on research, production, and review cycles”
Reported outcome

After migrating to the Claude Agent SDK, internal eval accuracy rose from 25% to 85%, context overflow failures dropped to zero, and each banker now manages five to eight concurrent deals; the firm closed eight deals totaling $91M in its first year.

Reported metrics
Internal eval accuracy (overall improvement)25% to 85%
internal eval accuracy after SDK switch only25% to 60%
internal eval accuracy after MCP and prompting improvements85%
context overflow API call failuresdropped to zero
Show all 17 reported metrics
internal eval accuracy (overall improvement)25% to 85%
internal eval accuracy after SDK switch only25% to 60%
internal eval accuracy after MCP and prompting improvements85%
context overflow API call failuresdropped to zero
concurrent deals per banker5 to 8
new potential client meetings per banker per day2 to 3
deals closed in first year8
total transaction value in first year$91M
average deal size increase (2026)$11M to over $20M
buyer list compute cost with agentroughly $200 in compute
buyer list cost with traditional teamupward of $12,000
deck production time with Archieabout an hour
deck production time with traditional team30 to 40 hours
agentic workflows consolidated12+
buyer sourcing agent autonomous run durationup to 4 hours
data sources connected to Archiemore than 20
prior provider context window limitmillion tokens
Reported stack
Claude Agent SDKArchieMCPDeal PortalClaude Code
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Source
https://www.anthropic.com/customers/offdeal
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

After migrating to the Claude Agent SDK, internal eval accuracy rose from 25% to 85%, context overflow failures dropped to zero, and each banker now manages five to eight concurrent deals; the firm closed eight deals…

What tools did this team use?

Claude Agent SDK, Archie, MCP, Deal Portal, Claude Code.

What results were reported?

Internal eval accuracy (overall improvement): 25% to 85%; internal eval accuracy after SDK switch only: 25% to 60%; internal eval accuracy after MCP and prompting improvements: 85%; context overflow API call failures: dropped to zero (source-reported, not independently verified).

What failed first in this deployment?

OffDeal had been using a long-context model from another provider, packing the full context into each call to preserve accuracy over RAG, but that approach failed when context exceeded the million-token limit.

How is this finance ops AI workflow structured?

Deal phase task initiated → Archie applies modular skills → Autonomous buyer sourcing → Personalized buyer emails drafted → Presentation decks generated.

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