AI‑Assisted Development Cuts FinTech Database Calls 70% in Weeks, Solifi Shows – In a joint press release, Solifi, the global secured‑finance software provider, and forgd, an applied‑AI consultancy, disclosed that an AI‑native development workflow slashed database calls by 70 percent within a three‑week sprint on a critical loan‑management module. The experiment, which embedded Anthropic’s Claude Code directly into Solifi’s engineering team, offers a rare, data‑driven glimpse of how generative AI can accelerate modernization across legacy‑heavy financial institutions.
From Question Marks to Embedded AI
Solifi’s engineering organization launched a rapid‑modernization drive aimed at tightening production‑grade resilience while trimming technical debt. Rather than a multiyear remediation, the firm paired its developers with forgd’s AI architects, who introduced Claude Code—a large‑language‑model (LLM) tuned for code generation and refactoring. Within three weeks, Solifi engineers were independently writing, testing, and deploying AI‑augmented code, achieving a 70 percent reduction in database round‑trips for a high‑traffic credit‑line service.
How the Technology Works
Claude Code operates as a conversational coding assistant, ingesting repository context, schema definitions, and test suites to suggest optimizations in real time. In Solifi’s case, the model identified redundant queries, consolidated data‑fetch patterns, and auto‑generated missing unit tests. The AI‑assisted workflow also integrated prompt‑engineering guidelines, enabling developers to frame “what‑if” scenarios—such as “replace N+1 queries with batch fetches”—and receive ready‑to‑merge pull requests.
Why It Matters for FinTech
Legacy platforms in secured finance often juggle monolithic codebases, strict compliance mandates, and high‑volume transaction loads. According to Gartner, 70 percent of software development teams will rely on AI‑driven tools by 2025, yet adoption stalls when only early adopters benefit. Solifi’s sprint demonstrates that a focused, capability‑transfer model can democratize AI benefits across an entire engineering org, cutting development cycles without compromising audit trails or security controls.
Competitive Landscape
The AI‑coding market is crowded. GitHub Copilot, Tabnine, and Amazon CodeWhisperer each claim productivity gains, but most operate as plug‑ins rather than embedded partners. Forgd’s model differs by embedding AI architects on‑site, tailoring prompts to Solifi’s domain‑specific data models, and delivering a hand‑off that leaves the client team self‑sufficient. This “co‑development” approach mirrors the consulting style of Accenture’s AI practice but with a narrower, code‑first focus, reducing overhead and accelerating knowledge transfer.
Implications for Enterprise Marketing Teams
For B2B marketers in the fintech space, faster release cycles translate into more frequent Enterprise Marketing announcements, A/B testing of new features, and tighter alignment with sales pipelines. An AI‑native development stack can shrink time‑to‑market for embedded‑finance APIs, allowing banks to launch white‑label lending solutions in weeks rather than quarters. Moreover, the data‑driven metrics (e.g., 70 % fewer DB calls) provide concrete proof points for go‑to‑market narratives, enriching case studies and boosting credibility with C‑level prospects.
The Road Ahead
Solifi plans to scale the AI‑assisted methodology across its broader platform, targeting risk‑scoring engines and compliance dashboards. Forgd, meanwhile, is expanding its service offering to include “AI‑first architecture reviews” for other fintech firms wrestling with legacy debt. As the industry leans into open‑banking standards and embedded finance ecosystems—think Salesforce Financial Services Cloud or Microsoft Azure’s Confidential Ledger—the ability to refactor code quickly while preserving regulatory fidelity becomes a competitive moat.
Market Landscape
The fintech infrastructure market is projected by IDC to reach $210 billion by 2027, driven by demand for real‑time payments, open‑banking APIs, and embedded finance solutions. AI‑driven development tools are emerging as a cost‑containment lever; Forrester estimates that LLM‑assisted coding can reduce development effort by 30 percent on average, delivering up to $1.2 million in annual savings for mid‑size software teams. Yet adoption is uneven: a 2023 McKinsey survey found that only 22 percent of financial institutions have integrated generative AI into production pipelines, citing concerns over model governance and data privacy.
Solifi’s rapid sprint addresses these concerns head‑on. By embedding AI architects within the client’s devops environment, the approach enforces internal compliance checks, version‑controlled prompt libraries, and audit‑ready commit histories—features that pure SaaS code assistants lack. This hybrid model could set a template for other regulated sectors, from insurance to capital markets, where AI must coexist with stringent oversight.
Top Insights
- Speedy ROI: A three‑week AI‑assisted sprint delivered a 70 % cut in database calls, proving that generative AI can produce measurable performance gains in legacy fintech stacks.
- Embedded Expertise Wins: Forgd’s on‑site AI architects accelerated knowledge transfer, turning early adopters into a self‑sufficient development team faster than typical SaaS tool rollouts.
- Market Pressure: Gartner predicts 70 % of dev teams will use AI by 2025, but Solifi’s case shows the missing piece is structured, domain‑specific integration—not just a plug‑in.
- Marketing Leverage: Quantifiable efficiency gains give fintech marketers concrete ROI figures to foreground in product launches and sales enablement.
- Compliance‑Ready AI: Embedding AI within existing CI/CD pipelines preserves audit trails, a critical requirement for regulated finance firms and a differentiator from generic AI coding assistants.
Get in touch with our fintech expert
