Solifi, the global secured‑finance software provider, announced that its engineering team slashed database calls by 70 percent within three weeks by embedding forgd’s AI‑assisted development platform—an achievement that showcases the practical power of AI‑native coding tools for enterprise‑grade applications.
Solifi’s latest press release highlights a rapid modernization effort that leveraged forgd’s applied‑AI consultancy to embed Anthropic’s Claude Code directly into the company’s development workflow. The partnership focused on a high‑traffic loan‑servicing module, where excessive database queries had long hampered performance and inflated operational costs. By week three, Solifi engineers were independently generating, testing, and deploying code with Claude’s assistance, closing critical test‑coverage gaps and delivering a 70 percent reduction in database calls.
The experiment is more than a headline‑grabbing statistic; it provides a concrete case study of how AI‑assisted development can accelerate legacy‑system refactoring—a pain point for many financial‑technology firms. “We moved from questioning whether AI‑native development was real to embedding it into how we operate,” said Vinay Mehta, Solifi’s CTO. “That shift took weeks, not the months or years a conventional approach would have demanded.” The speed of adoption mirrors findings from a recent Gartner survey, which predicts AI‑driven development tools will boost developer productivity by up to 30 percent by 2025.
How the Technology Works
forgd’s platform integrates Claude, Anthropic’s large‑language model tuned for code generation, directly into a team’s Integrated Development Environment (IDE). The workflow combines three core capabilities:
- Contextual Prompting – The model ingests repository metadata, recent commits, and domain‑specific documentation, allowing it to generate code that respects existing architecture and compliance rules.
- Automated Test Generation – By analyzing uncovered code paths, Claude proposes unit and integration tests, helping teams close coverage gaps without manual effort.
- Iterative Review Loop – Engineers review AI‑suggested snippets in real time, providing feedback that refines the model’s output for subsequent cycles.
In Solifi’s case, the AI suggested query‑optimizing patterns and introduced caching logic that eliminated redundant reads. Within three sprint cycles, the team reported a measurable drop in latency and a 70 percent cut in database calls—a result that aligns with IDC’s 2023 report indicating AI‑driven automation can reduce code defects by 40 percent.
Why It Matters for the Enterprise
Financial institutions operate under stringent regulatory and performance constraints. Traditional refactoring projects often span multiple quarters, consuming scarce engineering resources while exposing the organization to compliance risk. An AI‑assisted approach compresses that timeline dramatically, delivering tangible performance gains without extensive re‑architecting.
The broader implication is a shift in how enterprises view AI not as a peripheral add‑on but as a core development capability. As Morten Bagai, SVP of AI Engineering at forgd, noted, “Durable AI adoption happens when building with AI becomes the default for an entire engineering team.” This sentiment echoes a Forrester study that found 55 percent of large enterprises plan to integrate generative AI into their software development lifecycle by 2026.
Competitive Landscape
Solifi’s results position forgd’s Claude‑centric workflow alongside other AI coding assistants such as GitHub Copilot, Amazon CodeWhisperer, and Microsoft’s Azure OpenAI Service. While Copilot and CodeWhisperer excel at autocomplete and snippet generation, they often lack deep domain awareness required for regulated sectors like finance. Forgd’s model differentiates itself by embedding consultants who tailor prompts, enforce compliance checks, and coach engineers throughout the adoption curve—an approach reminiscent of professional services firms rather than pure SaaS tools.
Moreover, the integration of Claude, a model built on Anthropic’s safety‑first philosophy, addresses growing concerns around hallucinations and code reliability. Enterprises that prioritize risk mitigation may favor this safety‑oriented model over more permissive alternatives.
Impact on Enterprise Marketing Teams
Although the announcement originates from a development perspective, the downstream effects are relevant to B2B marketing teams. Faster release cycles enable product teams to iterate on features that directly support sales enablement—such as real‑time risk analytics dashboards or automated compliance reporting tools. Marketing can leverage the narrative of AI‑driven efficiency to differentiate their offerings in a crowded fintech market, highlighting reduced time‑to‑value and improved system reliability as tangible benefits for prospects.
Future Outlook
The Solifi‑forgd collaboration underscores a growing trend: AI‑native development is moving from experimental labs into production environments where performance, security, and compliance are non‑negotiable. As more vendors introduce domain‑specific prompt libraries and governance frameworks, the barrier to AI adoption will continue to fall. Analysts at McKinsey project that AI‑enhanced software engineering will contribute up to $1.2 trillion in economic value by 2030, driven largely by gains in operational efficiency.
Market Landscape
The AI‑assisted development market is projected to reach $12 billion by 2028, according to a recent IDC forecast. Key players—Microsoft, Google, Amazon, and Anthropic—are expanding their model APIs and tooling ecosystems, while niche consultancies like forgd focus on industry‑specific integrations. Financial services, healthcare, and aerospace are emerging as early adopters due to the high cost of legacy modernization. Regulatory scrutiny is prompting vendors to embed compliance checks into AI pipelines, a feature that gave forgd an edge in Solifi’s use case.
Top Insights
- Solifi’s 70 % reduction in database calls demonstrates that AI‑assisted development can deliver measurable performance gains within weeks, not months.
- Embedding AI consultants accelerates team proficiency, turning early adopters into organization‑wide practitioners faster than self‑service tools.
- Claude’s safety‑first architecture addresses compliance concerns that have limited AI adoption in regulated industries.
- Enterprise marketers can capitalize on AI‑driven development speed to shorten product‑to‑market cycles and reinforce value propositions.
- The competitive edge now lies in domain‑aware AI workflows rather than raw model size or generic autocomplete features.
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