Backbase Delivery Factory Aims AI Agents at Bank Builds
Backbase has moved Delivery Factory, a set of banking-specific AI agents that work alongside a bank's own delivery team from an agreed requirement through to tested code, into general availability across all markets. The Amsterdam vendor says teams using it across more than 10 of its implementations over the past year shipped twice as fast as traditional teams, placing the Backbase Delivery Factory squarely on the part of a banking software project that most reliably runs late: the build and rollout phase.
What is Backbase Delivery Factory?
Delivery Factory is an AI software factory that assigns a specialist agent to each role on an implementation team. Business analysts work with a Business Analyst Agent that turns scoping documents, specifications and workshop notes into user stories, acceptance criteria and Jira work items. Solution architects use a Solution Architect Agent to weigh design options and surface reusable services, while backend, mobile and web agents draft implementation plans tied to the bank's specific Backbase version. Engineers then take reviewed plans into their own coding tools, inspecting every change and verification result before work progresses through the bank's connected repositories and delivery pipelines.
The design principle is a single shared project context. Scoping documents, architecture specifications and meeting notes are combined with Backbase's own product knowledge into one reference that every person and every agent draws on, so a requirement agreed in a workshop is not re-specified at each handoff. Backbase reports that across the same implementations, teams cut requirements and specification effort by 80%, reduced coding and review effort by 40%, and lifted test and automation coverage by 60%.
Backbase paired the launch with two further agents. A Migration Agent moves existing journeys, integrations and business logic onto its Banking OS rather than rebuilding them, and an Upgrade Agent applies platform changes and flags what needs attention, folding version upgrades into routine delivery instead of treating them as separate projects.
Why is the build phase where banking software goes wrong?
For finance leaders, the figure that matters is not how fast a vendor demo runs but how often implementations miss their dates. IBM research into core banking modernisation found that 94% of banking overhauls extend beyond their planned timelines, with the resulting delays eroding return on investment. The Standish Group's long-running data on large IT programmes tells a similar story, with only around a third meeting their stated success criteria. Delivery and integration, not the underlying technology, are where the money and the calendar tend to slip.
That is the pain point Delivery Factory is built to address. By standardising how requirements, designs and tests move through a project, the pitch is a shorter, more predictable path from signed scope to working software, which in banking translates directly into earlier time-to-value and fewer stalled transformation programmes.
How does it differ from a generic AI coding assistant?
Backbase draws a line between its agents and off-the-shelf coding assistants, and claims a 30% speed advantage over teams using generic AI tools. A general-purpose assistant works from the code already in front of it. Delivery Factory is grounded in what a specific bank has agreed and in Backbase's own platform knowledge, so its output is shaped by the project's requirements and the bank's deployed version rather than by code alone.
Greg Fahy, chief technology officer for digital banking at Backbase, framed the benefit as giving human teams and their agents a shared foundation so they spend less time reconstructing context and more time delivering changes.
There is a second-order effect worth naming. Because the agents are trained on Backbase's product knowledge and run inside the bank's repositories and pipelines, the delivery layer itself becomes Backbase-shaped. Faster builds strengthen the commercial case, and they also deepen the platform relationship in a way that is harder to unwind later.
Where does this leave Backbase against rivals?
The timing is not accidental. Over 2026, the major core providers have each pushed agentic AI into their platforms: Fiserv with its agentOS layer, FIS with a financial-crimes agent, and Jack Henry with its own agent framework, each paired with a large cloud or model provider. Gartner has forecast AI software spending in banking and investment services reaching 55.2 billion dollars by 2027, and most of that attention has gone to agents that sit inside live banking systems and act on transactions.
Delivery Factory points at a different stage of the lifecycle. Where rivals are automating what a bank's software does in production, Backbase is automating how that software gets built and kept current. The company, founded in 2003 by Jouk Pleiter and recognised as a category leader by Forrester, Gartner and IDC, bootstrapped to a 2.5 billion euro valuation before taking external capital from Motive Partners in 2022, and now counts more than 120 banks as customers. For a vendor that competes on breadth of platform, controlling the cost and speed of implementation is a credible way to defend that base.
What should banks check before trusting the numbers?
Every performance figure attached to Delivery Factory comes from Backbase's own projects, with no independent benchmark published to date. The headline claims also sit alongside an earlier, lower number: Backbase has elsewhere described beta teams shipping 50% faster than traditional teams, against the "twice as fast" figure cited for the generally available product. Both are self-reported, and the gap between them is worth clarifying before any of the numbers are repeated as fact. Prospective buyers will also want to see how the agents behave on a legacy estate that looks nothing like a reference deployment, which is where banking implementations usually come unstuck.
Why This Matters to FinanceX Readers
The competitive front in banking software is widening from what AI does for the customer to what AI does for the people building the bank's platform. If agentic delivery genuinely compresses implementation timelines and cost, it reshapes vendor economics, shortens the payback period investors scrutinise, and raises the bar for any provider still quoting multi-year rollouts. The figures here are the vendor's own, so the signal to watch is independent confirmation from named banks on live programmes, not a demo. For anyone tracking banking technology budgets, the build-and-migrate layer is becoming the next place efficiency, and lock-in, are won.
