Thinslices

Most teams think AI development is about prompting. We learned it’s about structuring. https://hubs.li/Q0484x-g0

Six developers. Three days. Full focus on AI. We ended up with 100,000 lines of code and a system we couldn’t trust. https://hubs.li/Q0484mS_0

We ran an experiment: build with AI, as much as possible. The result looked impressive, until we tried to use it. https://hubs.li/Q0484lG40

We thought AI would accelerate development. It did, but not in the way we expected. https://hubs.li/Q0484BhG0 Our first attempt produced a massive amount of code… and a fragile system behind it. The turning point: We stopped treating AI as a generator and started treating it as part of a system. What actually worked: - Clear product context (not scattered docs) - Explicit technical constraints - Iterative rebuilds instead of incremental fixes Which of these have you seen work in practice?

We generated 100,000+ lines of code in 3 days. https://hubs.li/Q0484klp0 Almost none of it held up. The real lesson wasn’t about AI capability, it was about workflow design.

Fully autonomous AI sounds appealing. But in most SaaS workflows, it’s the wrong design. https://hubs.li/Q046bnvX0 Especially when outputs affect financial decisions, operations and customer communication. The more reliable approach is AI as decision support - practical product patterns: • AI generates drafts or suggestions • Users review or approve outcomes • Outputs remain editable • Decisions remain traceable This preserves two critical things: user trust and accountability.

AI behaves very differently from traditional application services. Yet many SaaS platforms try to integrate it the same way. That’s where architectural friction begins. https://hubs.li/Q046bmbv0 AI systems introduce: • probabilistic outputs • higher latency • heavy compute usage • evolving models and prompts Treating them like a typical API dependency creates instability. A more durable architecture usually includes: 1. Dedicated AI services Separate inference from transactional systems. 2. Explicit model versioning Models, prompts, and datasets should behave like versioned assets. 3. Asynchronous processing where possible Not every workflow requires synchronous inference. 4. Cost and latency guardrails Queueing, caching, and usage monitoring become critical. #SoftwareArchitecture #AI

Most SaaS teams try to add AI features. The teams seeing real value are embedding AI into workflows. That difference changes how the product gets built. https://hubs.li/Q046bjN_0 A practical framework we see working: 1. Start with a product problem Not a model capability. Identify real workflow friction. 2. Validate before deep integration Run thin experiments. Internal tools, limited releases, human-in-the-loop. 3. Separate experimentation from core systems AI prototypes should not destabilize production services. 4. Treat AI as a platform layer Dedicated services. Versioned models. Clear contracts. 5. Fix your data foundation first Structured events and reliable pipelines matter more than the model. 6. Design for human oversight Suggestions, drafts, approvals — not silent automation. 7. Treat AI as a living feature It needs monitoring, iteration, and ownership after launch.

AI in SaaS products often looks impressive in demos. But many of those features quietly disappear after launch. https://hubs.li/Q046b5l00 The reason is rarely the model; it’s the gap between experimentation and real product integration.

Excited to see our partners at Salt Edge heading to PAY360 in London 🇬🇧 At Thinslices, we’re proud to collaborate with Salt Edge to help fintechs and financial institutions build secure, scalable solutions powered by open banking and real-time data access. If you’re attending PAY360 (March 25–26, ExCeL London), make sure to stop by Booth G32 to connect with the Salt Edge team and explore conversations around Pay by Bank, VRP sweeping, bulk payments, strong authentication, and secure user journeys. #OpenBanking #Fintech #PAY360 #Payments #BankingInnovation #Thinslices #SaltEdge