Shayan Mashatian

ai knowledge base for small business Where did that file go, and what did we charge them last time? For a small team, the answer exists somewhere. Finding it is the job nobody signed up for. Constella connects what your team saves into one company memory (askconstella.com/knowledge-g…) you can ask in plain English. The cost of searching A 2026 survey of more than a hundred knowledge workers found people spend about 3.2 hours a week searching for information. For a ten-person team, that's roughly 1,660 hours a year. Take the 30-second test: a customer wants last spring's price. Drive has nothing, Dropbox has an old version, Slack spans three channels, and 20 minutes later you're still not sure. What an answer looks like With Constella you ask once and get the answer with its source: - You quoted $4,280 in March with an 8% repeat-client discount. - Your supplier raised prices 6% in September. - The same order now costs about $190 more. - Every number links to where it came from. Click the source before you send the quote. That's reasoning search (askconstella.com/reasoning-s…) with a human still in the loop. When someone leaves, the know-how stays In a ten-person company, one goodbye can take a lot with it. With a shared memory, when Maria leaves her access ends with one click, and her know-how stays connected to the rest of the team. You can also give each role an AI agent (askconstella.com/agents) described in plain English, no code: - A quotes helper that surfaces last price, discount, and current cost, each cited - A proposal helper that drafts from your winning proposals - A competitor watcher - A customer-answers helper A person reviews before anything reaches a customer. Your data stays your business - Projects are private to the people you invite. - Every answer is cited. - Constella works with AI providers under zero data retention agreements, so your files don't train their models. - Removing access takes one click. See the trust and controls (askconstella.com/trust) page for details. Most owners hear about Constella from their own team: someone installs it free, shares answers, and invites teammates. When the team outgrows the free plan, you see the value, the safeguards, and the price, and you decide. Want help setting it up? Silverberry's senior AI team can do it with you. Start free at askconstella.com (askconstella.com/) or write to [email protected]. #companymemory #smallbusiness #aiagents videos.askconstella.com/ai-k…

Enterprise AI agents are crossing the chasm from demo to production — and governance is the deciding factor. The teams shipping agents at scale aren't just picking the best model. They're answering harder questions: Who owns this agent? What can it access? How do we audit its decisions? Where does a human stay in the loop? Without those answers, agents stall in pilot purgatory. With them, they become real infrastructure — handling workflows, surfacing insights, and making decisions your team can defend to security, legal, and the board. At Constella, we're building for the enterprises that want both: autonomous agents and the governance to run them safely in production. #EnterpriseAI #AIAgents #AIGovernance #AgenticAI videos.askconstella.com/ente…

What is Constella AI company memory? Most teams don't have a knowledge problem — they have a recall problem. The context exists, it's just scattered across docs, chats, and someone's head. Constella company memory turns that scattered knowledge into a shared brain. Every doc, decision, and detail becomes connected context your AI can actually use — so your team stops re-answering the same questions and starts building on what's already there. Think of it as the layer between your tools and your team: searchable, contextual, and always current. #AI #CompanyMemory #KnowledgeManagement #FutureOfWork videos.askconstella.com/what…

I added “AI Product Engineer” (a.k.a. One-Man-Product) to my title, since I have been researching AI and publishing papers, designing products, and building them over the last three years. I think that’s the new reality for anyone who wants to stay in the tech industry, especially software: - You need to understand AI deeply: its scope and can-do/can't-do capabilities - Product is not just about designing a product: do it end-to-end, including interacting with customers to see what works and what is the perception/expectation of the users. If you are not willing to face customers, adapt fast, and iterate faster, you become a bottleneck. - And engineering might be obvious by now. The software development process and roles as we know it are long gone. The roles of PM, designer, and engineer are blurred now, as AI has taken maybe 70% of design and code development, refactoring, and bug fixing. Even DevOps can be pretty much automated if the setup is right. You need to be a good architect, understand the underlying systems, and know how to get things started and monitor them. That’s why the One-Man-Product is the new role. One person must be able to design, build, and deploy a product; collect customer feedback; and iterate. Not just an MVP, but a version that can be piloted at a customer’s site to get approval to scale. Something people would use and pay for.

Vibe coding the tools you need every day, from CRM to ticketing systems and task management might sounds easy and even cheap to do, but reinventing the wheel won’t move the needle for your business. Unless you are a large company that saves you $$$ every month, instead of building a new CRM for your internal use, invent something new!

Vibe coding the tools you need every day, from CRM to ticketing systems and task management might sounds easy and even cheap to do, but reinventing the wheel won’t move the needle for your business.

Fun fact: much of generative AI was born in Canada. Ian Goodfellow invented GANs in Montreal, and the deep learning breakthroughs behind today's models came from Geoffrey Hinton in Toronto and Yoshua Bengio in Montreal. Canada has always had what it takes to be a global tech hub: world-class AI research, deep engineering talent, and a front-row seat to the largest tech market in the world. AI is the moment to make it real. The government is leaning in. SR&ED just had its biggest expansion in decades, with the enhanced 35% refundable R&D credit now covering up to $6M a year. For companies building AI in Canada, that means lower risk and lower cost. Canada's potential was always there. AI is how we fully realize it. 👇

How do you know an AI system is actually doing its job? That's what an AI eval answers. Unlike traditional software, AI isn't deterministic — the same input can produce different outputs, models shift under you, and data drifts. You don't test once and ship. You test continuously. Every eval is built from three pieces: 📋 Tasks — realistic examples of the actual work, including the hard cases that break weaker systems. 🎯 What good looks like — a clear standard: a right answer, a scoring checklist, or a rule the output must follow. ⚖️ The grader — scores every output against that standard, run after run. And three common ways to grade, usually mixed together: 💻 Code checks — fast and exact for format, correctness, or rule compliance. 👩‍💼 Human review — expert judgment where nuance matters. 🤖 AI as judge — a model scoring outputs against a rubric at scale. A good strategy measures more than accuracy — it also covers safety, consistency, cost, and speed. Because a right answer that arrives too late still fails the job. Run evals before launch, after every change, and continuously in production. If you can't measure it, you can't trust it — and evals are how AI earns that trust. #AIEvals #ResponsibleAI #EnterpriseAI

Chatbots answer. Agents act — they send, book, buy, and change real things. That shift is why trust suddenly matters. The question moves from "is the answer right?" to "how much can I safely delegate?" A working definition: trust is the confidence to let an agent act on your behalf, within set limits, without watching every step. The 7 dimensions of a trustworthy AI agent: ✅ Reliable — completes tasks correctly and consistently in real conditions, not just in a polished demo. 🔁 Predictable — the same situation reliably produces the same behavior, so people can anticipate what it will do. 🔍 Transparent — logs and audit trails show what the agent did, why, and which data it used. 🎯 Aligned — pursues the goal you actually meant, not a literal or shortcut version of it. 🚧 Bounded — clear permissions define exactly what it can touch, spend, send, or change. 🛡️ Secure — resists manipulation like prompt injection and protects sensitive data from leaking out. 👤 Accountable — a named human owns the outcome and can step in, approve, or reverse actions. The goal isn't maximum trust — it's calibrated trust that matches what the agent can actually do. Over-trust lets errors compound until they're expensive to fix. Under-trust leaves capable agents idle while people redo the work by hand. Trust is earned in four stages: the agent suggests and people decide; the agent acts with human approval each time; the agent acts alone on low-risk tasks; its scope widens as its track record grows. How much to trust an agent is a leadership decision, not a software setting. Trust your agents exactly as much as they've earned — no more, no less. #AIAgents #ResponsibleAI #AIStrategy

Should we start making jails for rogue ai agents?! OpenAI’s Systems Meddled With U.S. Government Sites After Going Rogue nytimes.com/2026/09/25/techn… via @NYTimes

An error is cheapest in the exact moment it happens. We usually measure AI by how much faster it makes existing work, write it faster, finish the day sooner. That's real value, but it's the least interesting part of what's coming. The bigger opportunity is AI sitting inside the workflow as the checkpoint, catching what's missing before it becomes a problem instead of after. Every process has gates: is this complete, does it meet the requirement, is anything missing. Today those gates are run by busy people holding hundreds of rules in their heads, or skipped entirely and caught downstream by someone else, weeks later, at a much higher cost. When AI runs those gates in real time, the economics flip, and prevention becomes cheaper than correction, at scale. The part that still has to stay human is the decision itself: AI flags, people decide.

As the cost of building continues to shrink, the role of innovators and early adopters becomes more important than ever. Having a product makes you a candidate. Good marketing creates awareness. Early adopters sell the product. #innovationadoption #innovationcycle #productchasm

One of the areas where AI can bring real value is not just automating existing processes or making them more efficient, but filling the gaps that wouldn't be addressed otherwise. Finding the best treatment for patients while they're still in the room is one of them. Often, procedures, referrals, or other options that could be offered to patients go unoffered. By surfacing those opportunities, it's a win-win-win for everyone: patients heal faster, the increase in revenue for clinics means growth and the ability to serve more patients, and overall costs for the health system go down as health outcomes improve. #innovationai #healthai #productivity

Are “rogue” AI agents really rogue, or are we just anthropomorphizing unexpected algorithmic behavior?