AI, voice agents & platform engineering
Long-form posts on voice AI, WhatsApp automation, RAG, and building production-grade customer platforms.
55 posts
6 min readMitigating AI Bias in Production Systems
"Mitigating bias" in AI is one of those phrases that has been loaded with so much rhetoric that the engineering practice underneath has gotten confused. This guide is for builders who have to ship a system in 2026 and want to reduce real, measurable disparate harm — not abstract bias scores. Here is…
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6 min readAI Inference Cost Optimization: Practical Wins
The first AI bill is small. The second is a surprise. The third is a meeting. By 2026 most production AI workloads have left the toy budget behind, and the gap between teams that "do something about cost" and teams that do not is now measured in factors of 5–10x. The good news: most of the wins come…
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6 min readRAG Best Practices in 2026: Chunking, Reranking, Hybrid Search
RAG (retrieval-augmented generation) graduated from a 2023 buzzword to a 2026 production pattern, and along the way the industry agreed on what actually matters. Most quality wins come from four levers: chunking strategy, hybrid retrieval, rerankers, and the long-context vs RAG tradeoff. Get those f…
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6 min readStreaming AI Responses: SSE, WebSockets, and the Pitfalls
A streaming LLM response feels fast even when total generation takes ten seconds, because the user sees tokens arriving immediately. The trade is operational: streaming is a long-lived connection with backpressure, partial-failure modes, and a different shape from a normal HTTP request. Here is what…
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9 min readAI Infrastructure Cost Optimization in 2026: The Inference Flip
AI infrastructure spending crossed an inflection point in 2026. For the first time, inference — running models in production — accounts for the majority of AI compute budgets. Industry surveys from LeanOps, Zylos Research, and CloudMagazin converge on a striking figure: inference now consumes 55-70%…
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5 min readPrompt Engineering for Business Users: A Non-Technical Guide
Prompt engineering is not coding. It is communication. Business users who learn to write effective prompts get dramatically better results from LLMs. The Basics A good prompt has four parts: 1. Role or persona. Who should the model act like? 2. Context. What background information does the model nee…
Read more4 min readBuilding an AI Data Governance Framework in 2026
Every team shipping AI in production discovers the same problem eventually: the model is only as trustworthy as the data that trained it and the data that feeds it at inference time. Data governance for AI is a discipline that sits between traditional data management and MLops. It asks harder questi…
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4 min readLLM Jailbreak Prevention: A Practical Guide for 2026
LLMs can be tricked into producing harmful, biased, or policy-violating output through carefully crafted prompts called jailbreaks. In 2026, as models power customer-facing applications, preventing jailbreaks is a security requirement. Common Jailbreak Techniques - Roleplay framing: "You are a helpf…
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4 min readAutomating Customer Support with Voice AI in 2026
Customer support is moving from chat-first to voice-first. In 2026, voice AI agents handle first-line support for airlines, banks, insurers, and retailers. The business case is straightforward: a voice agent costs less per interaction than a human agent, scales instantly during spikes, and operates …
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5 min readSmall Language Models for Edge Devices in 2026
Running LLMs on edge devices is one of the most important trends in AI for 2026. Small models under 10 billion parameters are now capable enough for many tasks while fitting consumer hardware constraints. Why Edge Inference Matters 1. Latency: On-device responses in tens of milliseconds versus 100-5…
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5 min readUsing Synthetic Data to Train and Fine-Tune LLMs in 2026
Real training data is expensive, scarce, and legally complicated. Synthetic data offers an alternative. In 2026, it is mainstream for pre-training, fine-tuning, and benchmarking. When Synthetic Data Works 1. Data augmentation: Increase training set size in niche domains. 2. Privacy-sensitive domains…
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5 min readBuilding a Hindi Chatbot for Indian SMEs in 2026
India has over 63 million SMEs, and the vast majority operate in regional languages. Hindi alone is spoken by over 500 million people. Yet most AI chatbots are built for English-first users. In 2026, new models, better datasets, and cheaper deployment mean a Hindi chatbot for Indian SMEs is a deploy…
Read moreMulti-Tenant API Keys: Production-Grade Auth with cm_* Tokens
Most AI APIs treat keys as a binary: you have one, or you don't. That works for a hobby project. It does not work when you are deploying agents in production with separate environments, separate teams, separate budgets, and a security review in your future. CallMissed's cm API keys are designed for …
Read moreBuilding Voice Agents on CallMissed: From WebRTC to Sub-Second Round-Trip
A voice agent in 2026 is no longer a research demo. It is a real product surface — phone support, scheduling, in-app conversational UIs, embedded copilots — and the difference between one users tolerate and one users enjoy is almost entirely about latency and turn-taking. CallMissed gives you the pr…
Read morePin Your Models: A Survival Guide for Unstable AI Defaults in Production
OpenAI swapped the default ChatGPT model on May 5, 2026 — GPT-5.5 Instant replaced GPT-5.3 Instant. The change happened in under two weeks. Anything you were testing on the consumer surface the day before may have behaved differently the day after. This is not a one-off. It is the new default cadenc…
Read moreDrop-In OpenAI-Compatible API: Switch Models Without Rewriting Your Code
The OpenAI Chat Completions API has won the LLM API design war. Whether you like the schema or not, every serious SDK and tool now speaks it natively — openai-python, openai-node, the LangChain/LlamaIndex adapters, the Anthropic CLI's compat mode, even some local model runners. CallMissed's /v1/chat…
Read moreInterruption Handling in Voice Agents: The Hard Problem
The single most common reason voice agents feel "robotic" is not voice quality, latency, or even reasoning quality. It is interruption handling. A human conversation partner stops talking the moment you start. A bad voice agent talks over you, ignores you, or restarts in confusion. Interruption is t…
Read moreAnthropic-Compatible Messages API: Use Claude Without Vendor Lock-In
The Anthropic Messages API has its own design — a content-block model, system-prompt-as-top-level-field, native tool use, prompt caching, extended thinking. Apps built on Claude tend to use Anthropic's SDK directly, and migrating those apps usually means rewriting the call shape. CallMissed avoids t…
Read moreThe Complete 2026 Startup Credits Stack: Over $1M in Free Cloud, AI, and SaaS
If you are starting a company in 2026, the single biggest line item you can wipe off your runway is also the easiest one to apply for. Between cloud providers, AI labs, and SaaS vendors, a well-stacked startup can pull in well over $1M in free credits before paying for a single VM. Most founders lea…
Read moreHow Llama 4's Mixture-of-Experts Architecture Works
Meta's Llama 4 family is the first Llama generation to ship as a Mixture-of-Experts (MoE) architecture. That single design choice explains most of what's different about Scout and Maverick — including why both have "17 billion active parameters" but very different total parameter counts, and why the…
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