
A software factory, not a software agency
A software factory is a quality-gated line of senior engineers and AI agents, not bespoke projects, and why the mid-market should hire one.
Field notes from inside the work — GEO, agent deployment, evals, and what actually survives contact with production. Written by the engineers doing it, not a content team.

A software factory is a quality-gated line of senior engineers and AI agents, not bespoke projects, and why the mid-market should hire one.
Getting cited by AI answer engines
13 articlesWhat AI agents are, and which one you need
9 articlesHow to buy AI without getting burned
8 articlesWhat AI actually costs, and what it returns
7 articlesKnowing whether you are ready to build
3 articlesKeeping a live AI system working

Straight numbers with sources: what an ERP implementation actually costs to roll out, why the software licence is the cheapest line in the budget, and where projects really blow up. From a firm that publishes its prices.
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When ChatGPT and Perplexity disagree, a blended citation rate hides a zero on the engine buyers use. Score each assistant. Run the free AI Citation Audit.
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When to hire vs embed AI engineers: two senior seats open four months is the signal to stop recruiting. Embedded AI Teams start at $3,000–$7,500/engineer/mo.
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LLM-as-judge for AI agents is usable only after a human-labeled holdout calibration. Trust auto-scores then. Book the $25,000 AI Transformation Sprint.
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Build an AI agent eval set from real tickets. Stratify volume and failures, label correct answers, gate deploys. Book the $25,000 AI Transformation Sprint.
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AI agents for fintech payments pay off first on compliance gates that block release, with $41.2M Year 1 volume. Book the $25,000 AI Transformation Sprint.
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AI agents for consulting firms pay off first on knowledge search, proposals, and time capture — with published hours. Book the $25,000 AI Transformation Sprint.
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AI for consulting firms should start with the archive, not a proposal bot. One firm got 1,200 hours a quarter back. Book the $25,000 AI Transformation Sprint.
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A multi-step AI agent at 95% per step is about 66% end to end after eight steps. Split or gate it. Book the $25,000, two-week AI Transformation Sprint.
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Companies get excluded from AI answers when crawlers, published facts, or the brand name conflict. The page can still exist. Run the free AI Citation Audit.
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AI agent eval metrics that should gate every deploy: golden-set size, accuracy thresholds, confidence gates. Book the $25,000 AI Transformation Sprint.
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AI patient intake EHR integration that cuts admin time when eligibility, consent, and chart write are one path. Book the $25,000 AI Transformation Sprint.
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AI agents for ecommerce operations pay back first on returns, inventory, and exceptions — with published ROI. Book the $25,000 AI Transformation Sprint.
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Four cost centres, and the model is the smallest. What an AI MVP cost covers to reach real users, and the line most budgets forget.
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AI systems don't crash. They decay while dashboards stay green. Five ways a live system degrades, and what AI system maintenance actually is.
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An AI project ROI calculator is simple arithmetic. The hard part is four inputs, and three are inflated by whoever builds the case.
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A competitor shows up in ChatGPT when buyers ask. Five asymmetries explain almost every case, and four of them are fixable in weeks.
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Check AI visibility free this afternoon: a spreadsheet, four tabs, what to ask, what to record, and three mistakes that ruin a DIY audit.
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Not what one agent costs — the whole line item. How to size a mid-market AI budget across audit, build, and operate, and the split that survives year two.
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Third vendor, same line. Why switching AI vendors resets you to zero, what the last engagement should have left, and how to make this the last.
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An AI readiness assessment scores five dimensions: whether you can ship, what to fix first, what a good one contains, and when to skip it.
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Three phases, each ending in something that runs rather than something that reads. Why the order can't be rearranged, and what breaks when one is skipped.
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Seven entries, each a question someone asks at an inconvenient hour. The runbook that turns a live AI system into something a person can be on call for.
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AI system handover: the inventory to collect, the test that proves it worked, and the four things that always go missing.
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Not the vendor's tricks, your own. Seven internal line items that never appear in a quote, why the biggest is your team's time, and how to price them.
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Five tells in the first meeting, before any code. Each is a question you can ask, and the answers say whether an AI project fails or should be funded.
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How to get cited by AI: what must be true of your pages before a model can retrieve, quote, and verify you, and the check almost every site fails.
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Your Zapier flows and RPA bots aren't obsolete: they're cheaper and more predictable than an agent for the work they fit. The line is your exception rate.
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An AI agent should stop when an action is hard to undo, a mistake would go unnoticed, or it isn't confident. Four supervision patterns, and how to choose.
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You've decided to build custom. Hire AI in-house vs agency depends on whether AI is core to your product, and who operates it in year two.
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B2B AI search visibility: shortlists are drafted by an assistant before anyone calls. You can check in five minutes whether you're on them.
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How B2B buyers find vendors AI when review grids lose the click. Shortlist power moves somewhere less visible, and it changes who you influence.
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A one-page machine-readable summary of your site, in Markdown, at /llms.txt. What it is, what it isn't, and an honest answer on whether it does anything.
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Readiness isn't enthusiasm, it's five specific conditions. The checklist we run before we agree to build, and what to do if you only meet three of them.
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The headline number isn't the price, it's the entry fee for an open-ended meter. The five contract clauses that give the trap away, and what to ask for.
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The questions that separate a firm that ships from one that demos — what each is really testing, and what a bad answer sounds like. Ask ours the same ones.
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Product-market fit means the market wants what you built. Answer-Market Fit means its AI assistants recommend you by default. How to measure and reach it.
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There's no standard sticker price for GEO, but there is an honest answer: the three ways it is priced, what actually drives the number, and what we charge.
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Build vs buy AI agents: when to build in-house, when to buy, and the data on why most internal builds stall.
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A chatbot answers; an agent acts. The real distinction — autonomy, tools, and multi-step work — and how to tell which one your workflow actually needs.
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HIPAA compliant AI agents can run on PHI if the architecture is built for it: BAAs, minimum necessary, audit trails, and patterns that pass review.
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Citation rate is the share of your category's buying queries where an AI assistant names you. How to define it, measure it honestly, and what counts as good.
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How AI support automation SaaS hits majority autonomous resolution without torching CSAT: tier design, escalation, and evals, with real numbers.
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When a buyer asks an AI assistant for a vendor, the answer isn't ranked, it's assembled. What the engines retrieve, what they trust, and how to be cited.
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What an AI agent cost covers to build and run, why the token bill is the cheapest line, and where teams overspend. From a firm that publishes its prices.
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The six layers a production AI agent stack needs: model, orchestration, retrieval, evals, observability, guardrails, and where teams cut corners.
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Generative engine optimization is how you get cited when search splits into blue links and answer engines. Be the source an LLM quotes, not the one it skips.
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Pilot purgatory is an engineering problem, not an ambition problem. Here are the eval, ownership, and rollback gates that separate a demo from a deployed agent.
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A model you can’t measure is a model you can’t trust in production. How we build evals before we build the agent.
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What it actually means to embed engineers in your operation — and why it beats a deck-and-walk-away consultancy for the 20–500 person company.
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Support triage, onboarding, and renewal risk — ranked by time-to-value from real deployments.
ReadWhat changed in production AI this month. The signal, pulled from inside real deployments — not the hype feed.
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The agent patterns, model releases, and tooling that actually reached production this month.
Eval results and benchmarks from real deployments — what moved, what regressed.
GEO shifts: who answer engines started quoting, and why.
If you’d rather see it work than read about it, a 30-minute discovery call is the fastest path.