Some of your ads are paying to land people on broken pages.
- Dead ends
- Mismatched pages
- Slow on mobile
We build agents that watch paid search, fix what they find, and report what changed. This is one of them, running once, free.
01Rebuild the live keyword set
We reconstruct the paid keyword set the account is currently buying clicks on, and attach the demand and price signal behind each term.
- live paid keyword set
- monthly search volume
- estimated cost per click
- bounded ad-history window
02Collapse many-to-one onto destinations
Keywords map many-to-one onto landing pages. We reduce them to the unique destination set and roll each page’s terms and combined volume back up onto it, so no page is judged without the demand behind it.
- unique destination set
- terms rolled up per page
- combined volume attached
- highest demand first
03Resolve every destination live
Each page is requested the way an ad click would request it, concurrency-limited and backoff-aware, and we walk the entire redirect chain to where it truly resolves rather than trusting the URL in the ad.
- live HTTP request
- full redirect chain
- final resolved URL
- rate-limit aware retries
04Read the page, not just the status code
Two layers run over every resolved page: what the server returned, and what the page actually says. A healthy 200 reading sold out or offer expired still fails the click, and so does a page that renders perfectly but has nothing to do with the ad that sent it.
- transport-layer faults
- content-signal faults
- ad-to-page semantic match
- modelled mobile render
05Price each finding and rank on it
Findings are not sorted by severity in the abstract. Each one is priced from the demand routed into it, and the report opens on whichever problem is quietly costing the most every month.
- volume against paid CTR
- estimated monthly waste
- keyword evidence attached
- sorted by spend at risk
Runs in a few minutes, and a person reads every report before it reaches you. The first one is free. After that we can keep the report live against your account and refresh it on whatever cadence you want. Talk to us.
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What we've learned scaling AI inside real organizations.
Enablement, governance, agentic platforms, and the human side of adoption. Plain, practical, and grounded in the work.
Choosing a partner or pricing the work? Start with the buyer's guides.
Interactive, working pieces of the systems we build, embedded right on the page. Not a screenshot: drag them, click them, ask them something.
Give your front office one shared Bot, not five copies of it.
Grok Bot's new Team Bots let one person build a specialist Bot, like the one that knows how your company quotes, and publish it to the whole team. Everyone talks to the same Bot, each person's chat stays private, and when a rule changes, it changes once. Live artifact: one five-person office set up three ways, and what happens when the founder changes a rule.
Open the artifact →Before you let an agent work overnight, decide what it waits on you for.
OpenAI's new Agents API makes it easy to buy an agent that keeps working for hours in its own sandbox. For a small business, the setup that matters is the short list of things it can't finish without you: the refund over your limit, the payment to a vendor's new bank account, the reply that promises a customer a credit. Live artifact: drag through one night and decide what the agent left on your desk.
Open the artifact →Claude just made governance and distribution a first-class path for agent skills.
Orgs used to shoehorn governance and distribution through Slack, Drive, and GitHub. A native org library with review changes that for teams that need to move fast without losing the plot. Live publishing policy console: Requires review, Open, or Off.
Open the artifact →Most AI projects stall for a boring reason.
Teams buy more tools and more agents before they build shared memory. The work breaks because nothing is grounded in how the business actually runs. Live schematic: scattered sources, connected memory, people and agents on the same context.
Open the artifact →There's no looking back after Grok Bot.
A durable place for people to work with agent teammates, plus quiet automation that keeps the business running. You need both. Live artifact.
Open the artifact →How small can a real company be now?
A one-person or few-person company has never been more viable. The five-layer architecture that replaces the early org chart, live on the page: click through the stack, then slide the model mix and watch what open-weight models do to the monthly bill.
Open the artifact →Your company's most valuable asset is its working memory
A small, working knowledge graph you can drag, hover, and query, live on the page. Scattered meetings, docs, and decisions become one connected, queryable asset that your team can ask in plain English and your agents can call in structured terms.
Open the artifact →Before you hire anyone or buy anything: how to choose a partner, what the work costs, and how the tools compare.
How to choose an AI consultant: 7 questions
What to ask before you sign: what they have run, what they resell, and who owns the work.
Read the guide →Hire an AI consultant, or build in-house?
A plain decision framework: whether AI is core, your in-house depth, time to value, and the hybrid most teams miss.
Read the guide →Claude vs Copilot for business teams
There is no universal winner. Where each fits, and why the system around the model matters more.
Read the guide →Pick a model. Don't bet the system on it.
Why the model should be a setting you can change, not the foundation, and the one question to ask before you build.
Read the guide →How to build an agentic organization
AI agents doing real work, grounded in your own context and kept honest by measurable quality and human oversight.
Read the guide →How much does AI discovery cost?
What drives the price, open-ended versus capped, and whether you keep the plan.
Read the guide →What does an AI strategy roadmap cost?
What is inside a starting AI Playbook, and how AI consulting is priced.
Read the guide →AI consulting FAQ
Straight answers to the questions teams ask before they engage an AI consultant.
Read the guide →Which AI model you pick is now your smallest decision.
Google Cloud now offers all three leading closed AI models on one platform and one bill. When every top model is on the same shelf, picking one stops deciding much. What decides the result is which work gets rebuilt around AI, and who builds and runs it.
Read the piece →Drawing the chart just got cheap.
Claude and ChatGPT can now build the chart inside the assistant. The tools and shops that sell that drawing are about to feel it. What is left is the data, the infrastructure, and the agents behind the number.
Read the piece →Score the next step. Do not write a paragraph about it.
A closed choice should return a score you store with the work, and a weak score should stop the path instead of becoming a paragraph.
Read the piece →Pick a model. Don't bet the system on it.
Models get delayed, cancelled, repriced, and retired on schedules no customer controls. Build so the model is a setting you can change, and a model release becomes news instead of a rebuild.
Read the piece →The human is still the point
Everyone has the same models, agents, and prompts now. The rep still closes the call, someone still decides what's sensitive and what ships, and slop is what happens when a person removes themselves from the loop. The more powerful the AI becomes, the more the human matters.
Read the piece →Should your company brain be a curated wiki, or a queryable memory?
Two architectures for company memory, and one decision you cannot take back: keep the source, or keep the summary. What a curated, human-reviewed library does better than what we build, what it can never do, and the question to ask before anyone starts building.
Read the piece →We built a knowledge layer that fixes itself. Here is what it caught.
We run a universal knowledge layer on our own firm: rebuilt nightly, scored daily by an independent check, proposing its own fixes for one-tap approval. In its first week of self-auditing it caught four problems we would not have found on our own.
Read the piece →How much does AI implementation actually cost?
Cost follows scope, and scope is a choice. The three pricing models you will encounter, the four factors that drive the price, and how to structure the spend so every dollar is tied to a working deliverable.
Read the piece →The ROI tree: how to measure AI ROI immediately
You do not have to wait a year to measure the return on AI. A two-layer framework from real engagements: velocity and capability gains first, then the business metrics that follow with intention.
Read the piece →Everyone is a builder now: scaling AI across non-technical teams
Scaling AI inside a large organization is a human exercise long before it is a technical one. A field-tested framework: the human element, then scalability, then innovation.
Read the piece →