Insights, updates, and thought leadership on conversational AI, enterprise automation, and the future of customer experience
21 banking AI use cases, live or in delivery, across the Middle East, Africa and Europe: dialect voice, digital humans, KYC, fraud screening and outbound agents.
The 95 percent pilot failure stat is making the rounds again, this time with a diagnosis I actually agree with: chatbots automate sessions, but a bank runs cases. Why the unit of work decides whether your AI survives production, and the day eight test that tells you which one you built.
Chargebacks are heading for 337 million a year, most of them aren't real fraud, and each one costs a multiple of its face value. A practical look at the six AI jobs hiding in one dispute, what each of them needs to be governed, and why agentic commerce will change the volume.
Our banking use case map grew from 21 to 81 jobs, so we turned it into a playbook. Along the way the real story changed: the destination is the AI bank, where the work itself runs on governed AI, and the thing that decides who gets there is not the models. It's governance.
A brief landed on my desk asking for 600,000 outbound calls a month, by AI. The pattern is not a banking story: it applies to every enterprise with customers worth calling. Why human dialling died, what insurers, banks, lenders and software companies already run, and the anatomy of a governed outbound call.
Our newest product turns a bank's own transaction data into a wellbeing score, plain language insight and gentle nudges toward healthier money habits. It runs inside the bank, it measures its own impact, and it will never give financial advice. Here is how it works.
Per seat licensing carried software for a decade, but agents don't sit in seats and token pricing breaks the moment AI succeeds. Where AI pricing lands.
Procurement wants us to be a software vendor or an implementation partner. We are neither, and here is why the model in between gets AI into production.
62 AI model versions got a retirement date this year. Why locking your business to one model is a quiet risk, and why staying model agnostic wins.
Native speech-to-speech demos beautifully then fails enterprise needs. Why the text layer, where guardrails and audit live, still wins regulated voice.
A plain roundup of five things we shipped this year: emotional voice, interactive cards, agentic tasks, in-chat payments and response feedback.
Five takeaways from Mastercard's summit in Johannesburg: the technology is no longer the bottleneck, governance, trust and context are.
Signals from VivaTech and Web Summit Rio: production over pilots, substance over spectacle, and why your operating framework beats any one model.
Hybrid search, reranking, ColBERT, GraphRAG, self-RAG: a plain language guide to what each RAG variant does and which ones you probably don't need.
A knowledge assistant that aces its pilot can fail silently, recall sliding while dashboards stay green. Why good RAG rots after the demo.
Agentic systems promise autonomous workflows, but speech and dialect quality fail first. Why Arabic, Turkish, Chinese and more hit the same gap.
MOS has saturated and demos are hand tuned. The lab protocol we use to grade TTS on latency, jitter, entity accuracy, barge-in and cloning risk.
You already delegate spending with cards, POs and dual sign-off. An AI agent is a new delegate. How to write its mandate, and where the analogy breaks.
A dashboard tells you the plane landed. A control tower keeps two aircraft off the same runway in real time, with the authority to divert one. Here's what that distinction means for running AI agents in production, and why an HTTP 200 will lie to you.
Start with one agent, and reach for multi-agent only for parallel, read heavy work. The case against splitting too early, and how to do it right.
Most MCP breaches needed no exploit code; the servers behaved as specified. What goes wrong in production, and why the new OAuth spec is half a fix.
Most teams treat EU AI Act compliance as a legal side project. A checklist of what enterprises should implement now to avoid disruption and rework.
AI performance is more than accuracy. The production KPIs that matter, including latency, P99, TTFT and error rates, and how to measure them.
Text-to-speech is core AI infrastructure. What TTS engines are, the model approaches, the providers that matter, and why speed and dialect decide.
AI hallucinations are business risks, not just errors. What they are, real examples, practical mitigations, and the value of getting it right.
Introducing Agentic AI Studio: a platform agnostic, enterprise ready way to design and run autonomous workflows with your own models and data.
MCP servers standardise how LLMs discover and use tools. What they are, why they matter, how we use them at Blits.ai, and where this is heading.
Why commerce as we know it does not work for AI and what the Universal Commerce Protocol is trying to fix
Why giving AI agents controlled access to payments changes everything. The question is who designs the rules under which it does.
In the first week of January we turned quality assurance into a Bug Hunt Game. Here’s what happened when we put Hunters, Fixers, and a Judge on a shared leaderboard powered by Azure DevOps.
Blits.ai joins LLMs4EU. Who builds large language models, on what data, and under which European values and regulations, and why that matters now.
A plain guide for business teams on how to add your company data to an LLM safely, without leaking secrets, and the concepts behind it.
Let's be honest: I think it's great that technology is so embedded in our daily lives. It helps us get knowledge faster, complete tasks more efficiently, gives us inspiration, and occasionally scares the hell out of us with those crazy (fake) videos. I help a lot of companies implement AI, so in the end—it pays my bills. But after spending a ridiculous amount of time with all these new technologies, I feel it's time to reflect on the things I really hate about AI.
In the world of AI, where innovation moves at breakneck speed, being the CTO of a fast-growing scale-up is an exhilarating job—but not for the faint of heart. Our company specializes in large language models (LLMs), chatbots, digital humans, and voicebots, and we serve mainly large banks, international companies, and professional enterprises.
Blits.ai now supports GPT-4, bringing new capabilities to chatbot development and deployment. What it means for conversational AI.
Someone once told me, stupid questions don't exist. This might be true, but people are still afraid to look stupid by asking questions that are assumed to be general knowledge. We are currently at a point in time, where most people are assumed to know what is meant by the term Artificial Intelligence (AI) and how it works, but less is further from the truth. With the recent boost of generative AI services like ChatGPT and Midjourney, the spark is ever more lit, everyone loves to talk about it but I only know a hand full of people who actually know how it works.
Over 5 years ago I started a chatbot (ad)venture called Blits.ai because I believed the way we as humans interact with data will change to a more conversation-based approach. With the current ChatGPT hype (generative AI), more and more people are coming to the same conclusion which is great, but we are not clearly there yet.
Our research into conversational AI performance shows significant gains in chatbot effectiveness across multiple industries and use cases.
GPT-3 has revolutionized the field of conversational AI, but understanding its practical applications and limitations is crucial for successful implementation.
At Blits.ai we're introducing a new method that reshapes how chatbots work. From the beginning, our company has been focusing on building a chatbot ecosystem that gives companies access to the best performing engines of the market. Our current offering is both a middleware layer and a low-code software platform, that gives our customers access to 40+ cognitive AI services.
Building effective chatbots requires more than just basic conversation capabilities. Here are 15 advanced features that can transform your chatbot from a simple Q&A tool into a powerful business asset.
NLU is often a black box, but understanding how it works is key to building effective conversational AI. A look inside.
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