OffDeal consolidates 12+ agentic workflows into one Claude Agent SDK-based M&A advisor
OffDeal, an AI-native investment bank serving founder-led businesses with $1-20 million in annual profit, migrated from a long-context model to the Claude Agent SDK and consolidated over a dozen separate agentic workflows into a single general-purpose agent named Archie. The switch raised internal evaluation accuracy from 25% to 85%, eliminated context-overflow API failures, let each of its four bankers manage 5 to 8 concurrent deals, and produced a buyer-sourcing list that used to cost $12,000 and a week of analyst time for about $200 in compute. The firm closed 8 deals totaling $91 million in its first year with a team of two bankers.
Overview
OffDeal, an AI-native investment bank serving founder-led businesses with $1-20 million in annual profit, migrated from a long-context model to the Claude Agent SDK and consolidated over a dozen separate agentic workflows into a single general-purpose agent named Archie. The switch raised internal evaluation accuracy from 25% to 85%, eliminated context-overflow API failures, let each of its four bankers manage 5 to 8 concurrent deals, and produced a buyer-sourcing list that used to cost $12,000 and a week of analyst time for about $200 in compute. The firm closed 8 deals totaling $91 million in its first year with a team of two bankers.
This entry has 12 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Before the Claude Agent SDK, each OffDeal workstream (seller lists, buyer lists, confidential investment memorandums, and more) ran on its own dedicated agentic workflow that did very poorly on problems outside its predefined instructions, and every time a standard operating procedure changed, the corresponding workflow broke. The deepest constraint was context: a single deal can involve more than a hundred million tokens of buyer profiles, past transactions, financial documents and market data, and the team's long-context model from another provider simply failed once API calls hit its million-token limit.
The solution
OffDeal consolidated nearly all of its AI workloads into Archie, a single general-purpose agent built on the Claude Agent SDK, connected to more than 20 data sources and handling work across every phase of a deal via modular, self-contained "skills" that Archie can combine and apply flexibly rather than hardcoded per-workflow logic. A buyer-sourcing skill encodes an experienced banker's roughly 10 sourcing approaches and runs autonomously for up to 4 hours, cross-referencing findings and recursively refining its search, while non-engineers, including a banker, can build new skills like branded presentation generation without engineering help.
Reported business value
Switching to the Claude Agent SDK and then refining MCP design and prompting raised OffDeal's internal eval accuracy from 25% to 85%, and context-overflow API failures dropped to zero overnight. Each banker now manages 5 to 8 concurrent deals and meets 2 to 3 new potential clients per day, a buyer list that used to take a team of analysts more than a week and upward of $12,000 now costs about $200 in compute, and a branded deck that used to take 30-40 hours now takes about an hour. The firm closed 8 deals totaling $91M in transaction value in its first year with a team of two bankers, and its average deal size climbed from $11M to over $20M in 2026.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other financial services entries in the register.
Navy Federal Transforms Service With AI
Navy Federal Credit Union is reshaping banking for military members by unifying data and leveraging generative and agentic AI on the Databricks Data + AI Platform. By embracing AI-augmented workflows and upskilling teams, Navy Federal delivers customized services while streamlining productivity through responsible change management and data readiness.
Lloyds Banking Group cuts mortgage income verification from days to seconds with ML
Lloyds Banking Group, the UK's largest digital bank, migrated 15 modelling systems from on-premise infrastructure to Google Cloud's Vertex AI (now Agent Platform), giving over 300 data scientists and AI developers scalable machine learning capabilities. In six months the bank ran 80 new ML experiments and launched 18+ GenAI systems into production, including an algorithm that reduces the income verification step in mortgage applications from days to seconds. The migration also cut unplanned ML platform downtime to zero and saved 27 CO2 tonnes of operational emissions.
Banking Innovator bunq Supports Growth, Strengthens Security Using AWS
bunq, a Dutch neobank with over 11 million users across Europe, uses Amazon Bedrock for several generative AI use cases including summarizing new user data with large language models, removing the need for agents to process onboarding documents manually. Using Amazon Bedrock, bunq tripled user support process efficiency while maintaining over 90 percent accuracy. Sensitive data stays within bunq's AWS virtual private cloud, supporting GDPR and PCI DSS compliance alongside tools such as AWS CloudHSM, AWS Security Hub and AWS KMS.
TBC Bank Operationalizes Trusted Data with Lakebase
TBC Bank, the largest banking group in the Caucasus region, built a Lakehouse on Databricks and adopted Lakebase and Databricks Apps to move from on-premises SQL Server instances and month-long reporting cycles to self-service analytics and AI-driven applications, including a web-based AI chatbot and AutoML-based credit risk scoring. Credit risk model deployment fell from 14 weeks to two days, and more than 600 users regularly query governed data through Genie.
Was this helpful?
Your feedback helps us improve our use case database

