For technology companies
Content marketing for technology companies, built on your experts.
You already publish more than any other sector. That is the problem. Buyers have learned to tune it out, and the model they build a shortlist with was trained on it. Whether you sell software or sell expertise, Findwell puts your actual engineers and consultants on the record instead.
- A hub, and the team that fills itSoftware on your domain with a page per expert, written from their words and nobody else’s
- Experts who get namedRanking on Google, quoted in the trade press, cited when a buyer asks an LLM
- From £1,000Per expert a month, six month minimum, no setup fee
What Findwell is
A platform for content, and the agency that writes it
Two things that are usually bought separately, and that only work together. Here is the whole proposition in one line.
The work
An agency that gets it out of their heads
We interview your founders, CTOs and principal consultants, shape content for their voice, put it through your review and pitch it out.
The place
A platform of your own to hold it
A hub on your subdomain with a page per expert, structured so search and AI can attribute it to a person.
Findwell
The platform, with the people who feed it
Live in about two weeks, on an hour a month from each expert.
What it is
A page for every expert, built around the problem they solve.
On a subdomain of your own site. What they have actually shipped, what they got wrong, and what they would do differently, structured so search engines and answer engines can attribute it to a named human.
Step through what the page does
We record your expert, we draft from what they said, they approve. Nothing here is generated, which is increasingly the only thing that stands out.
The bits marketing will ask about
A dashboard behind the hub: what each expert ranks for, who is reading, which companies are visiting, and what the coverage earned.
Positioning and category language agreed before anything is written, your style guide, and a named sign-off before anything is published.
Where we secure coverage for technology clients
What you get
Everything an expert needs, designed around them.
One expert conversation becomes a complete content system. We create the content, and place it where it matters, building their page with every piece.
Shapes everything below
Published, then on the hub
Lives on the hub
Posted, then links to the hub
Sent, then kept on the hub
Cut, then hosted on the hub
See what your experts would rank for.
A thirty-minute walkthrough on your own firm. We will show you where your experts sit today, and what the first quarter would produce.
How it works
One hour a month. We do the rest.
The whole commitment from each expert, in one picture.
We ask nothing more. One conversation, one draft to read, and nobody opens a document at the weekend.
Forty minutes on a call
Recorded, with an editor who can challenge the first answer and draw out the insight behind it.
Fifteen minutes on a draft
We draft from the recording, check anything customer-specific or security-sensitive, and hand it to your review. Experts approve rather than write.
No weekend writing
Published on your hub, pitched to the tech desks that cover the category, then cut for LinkedIn, the briefing and video.
A worked example, not a client
What a year looks like for one expert.
The plan we would put in front of an AI engineer in week one, quarter by quarter. Every firm can see exactly what the retainer creates.
The specialist
Eleven production deployments, and a LinkedIn headline that says she is passionate about data.
The niche
Why enterprise AI pilots never reach productionNarrow enough that one person can own it, broad enough that CTOs, heads of data and the consultants advising them are all searching it right now.
Why that one
2,900searches a month across the cluster, and almost no expert-level pages answering any of it
Pick the problemStep 1 of 5
Once, before anything is published
Content strategy
Agreed with Dana and with BD before a word is written, so everyone knows where they stand from the beginning.- What problems Dana can own, and how narrow to go. Data and AI are far too saturated, but getting a pilot into production is winnable
- The problems worth answering, ranked by search demand rather than by what the category deck says
- The terms we are going after, and the invented category words we are not
Biography and expert page
Not the team page bio and not the conference speaker blurb, neither of which says what she has actually built- The deployments Dana can talk about, and the ones under NDA
- What Dana is known for, in the words a head of data would use
- Structured so search and answer engines can attribute it to a named engineer rather than to a company
Social refresh
It is where buyers, analysts and candidates all check, and on most technology sites the engineers are invisible- Headline and about section rewritten around the niche rather than the firm
- Featured section pointing back at the hub
- Banner and profile consistent with the expert page
Live in roughly two weeks, without needing anything from your IT department. We won't publish anything, or go near risk and compliance until we've agree with Dana on what her area of expertise will be.
First engineer on the recordStep 2 of 5
Every month
3 insight extractions, one a month
Every piece of content below is built from a single recorded conversation with one of our experienced editors- The argument Dana makes to every team that scoped the data last
- What changed this year in practice rather than in the release notes
- The failure Dana keeps seeing, and the meeting where it starts
3 newsletters, one a month
Written from the interviews and sent to Dana’s own contacts, then republished on the hub so the cluster and the client list grow together- Why your pilot never reached production
- Evaluation without a benchmark, and how to do it anyway
- The data problem nobody scopes until it is too late
1 media piece: an op-ed
We start what Dana can own. The firs piece is placed on our own schedule, not inserted into someone else's story- “The AI pilot problem was never a technology problem”, in The Register
5 video clips filmed
One sitting on an interview day, so the whole year of video costs no extra expert time- The three reasons pilots stall at handover
- What evaluation actually means, in plain English
- Retrieval, and when you genuinely do not need it
- Build or buy, and the questions to ask first
- What happens when the model changes underneath you
LinkedIn, as it comes
Commentary that makes sense for Dana, and posts whenever there's a new piece of content. Not a fixed calendar.- A post for every piece we publish
- A reaction on the days a major model or pricing change lands
- Comment on a benchmark claim while people are still arguing about it
Indexed within the month. Nothing ranking yet, and anyone promising otherwise on a new hub is guessing. What usually happens first is recruitment. Engineers read it before buyers do, and your recruiter starts sending the page to candidates. That alone tends to pay for the quarter.
Cover the whole failureStep 3 of 5
Every month
3 insight extractions, one a month
The content follows the expertise: the issues Dana is working through, explaining and arguing each month- What a year of production deployments has actually taught us
- Where budgets are being cut, and what that looks like early
- The build or buy question Dana gets asked most
3 newsletters, one a month
Each republished on the hub. Six pieces in, and the cluster starts to join up rather than sit as separate posts- What a deployment actually costs to run at scale
- Retrieval, and when it is the wrong answer
- The three things that kill a project at handover
1 media piece: an interview
We pitch Dana into stories journalists are already working on, so she is there when they need an expert voice. Harder to earn than an op-ed- Interview with Computer Weekly on what a year of deployments actually taught us
Video running
Captured in the quarter one sitting, then published fortnightly, one question at a time- One clip a fortnight, on the hub and on LinkedIn
- Each one answering one question and nothing else
- Subtitled, because most of it is watched on mute
LinkedIn, as it comes
A steady stream from Dana's published pieces, with timely posts whenever the news moves into her territory- A post for every piece we publish
- Reaction on the days the numbers or a judgment land
- The occasional short answer to a question Dana keeps being asked
The first enquiries come from people already talking to a competitor, who found the piece while checking something. That is the point at which you are in the comparison rather than outside it.
Cited by buyers and modelsStep 4 of 5
Every month
3 insight extractions, one a month
We capture one of these on camera, rather than asking for a separate filming session for our second set of clips- What a benchmark can and cannot tell you
- The cost conversation that happens after go-live
- What the vendor benchmarks actually show, under the headline
3 newsletters, one a month
Each republished on the hub. Nine pieces in, and the niche is most of the way covered- What good looks like six months after go-live
- Model changes, and how not to be broken by them
- Reading the vendor benchmarks properly
1 media piece: broadcast
Ready when the story breaks and a reporter needs an expert now. This is what the first two quarters build towards, and no firm can purchase it- Podcast or radio comment on the AI story running that week
5 more clips filmed
One session in quarter three covers the rest of the year- Cost at scale, and the bill nobody models
- Can you evaluate anything without a benchmark
- The vendor benchmarks, and what they really show
- Fine-tuning, and when it is genuinely worth it
- Talking to your board about an AI budget
LinkedIn, as it comes
The commentary starts travelling beyond the client list, into journalists' inboxes- A post for every piece we publish
- A same-day reaction when a major model or pricing change lands
- The clips, one at a time, as they come out
Journalists start calling rather than being pitched. And when a buyer asks an assistant which consultancy or platform to use, a named engineer with a year of attributed writing behind them is something the model can actually return.
Own the production questionStep 5 of 5
Every month
3 insight extractions, one a month
The last run of the year is the moment we choose the next area for Dana to own- What changed this year, and what did not
- Where Dana thinks the budgets land next year
- The question Dana wants to be known for in year two
3 newsletters, one a month
Including the annual guide, which brings the year’s thinking together on one page and becomes the strongest-ranking asset- Getting AI into production, the annual guide
- What actually changed this year, and what did not
- Where the budget goes next year, and where it stops
1 media piece: a year-ahead op-ed
Placed at the turn of the year, when every desk is looking for a forecast and few experts are willing to make one. By then, Dana is already a recognised name for the City desk- “The AI projects that will quietly be cancelled next year”, pitched to a national
Video running
From the quarter three sitting, still one a fortnight- One clip a fortnight, on the hub and on LinkedIn
- The annual guide cut down into a two-minute explainer
LinkedIn, as it comes
Posted alongside each piece, and the year-ahead piece will likely outperform anything else Dana has published- A post for every piece we publish
- The year-ahead argument, in Dana’s own words, ahead of the op-ed running
Once the cluster covers the full question, it stops attracting traffic and starts attracting buyers who already understand the position before they make contact
What that adds up to
12 hours
Of the expert’s time across the year. Twelve insight extractions, one a month, and a read of the drafts. The filming happens on two of those same days, so it costs nothing extra.
Across what you actually sell
The model is the same. What we publish is not.
A security engineer and a data platform lead are evaluated by different people, at different points, against completely different fears.
Nobody is asking what AI is any more
We help data and AI leads put their real-world experience into content, from evaluation guidance, production readiness explainers, cost commentary and the honest post-mortems nobody else will publish.
The market has moved from curiosity to scepticism, and it moved fast. Buyers have already run a pilot that went nowhere and they are now searching for why. The difficult questions around evaluation, data quality and cost are exactly where your expert has the strongest point of view
The team behind it
You are hiring people, not software.
Findwell is a product of Profile, an award-winning thought leadership and PR agency. So is the team.
“Technology is the only sector where the answer to a credibility problem has been to publish more. Two posts a week, written by somebody who has never used the product, and now largely generated. Buyers stopped reading it years ago and the models are trained on it, which is a genuinely bad loop to be inside. The way out is boring and it works: put a real engineer on the record, with their name on it, saying something specific enough to be wrong.”
Jordan GreenawayFounder, Profile
Questions
What firms ask before they start.
+How much does content marketing for technology companies cost?
+We already publish two blogs a week. How is this different?
+Will this get us cited when a buyer asks an assistant for a shortlist?
+Is this SEO, AEO, GEO, PR or thought leadership?
+How does this sit with our analyst relations?
+Our engineers will not write. Is that a problem?
+How long before our experts rank for these problems?
+What happens to the content if someone leaves?
+Can we start with a couple of people rather than the whole team?
+Why not just use an AI writer, or hire someone in-house?
See what your experts would rank for.
A thirty-minute walkthrough, on your own firm, of where your experts sit today and what it would take to move them.