Fast, secure websites
Project, then managed
Off WordPress, Drupal or Joomla. Onto Astro, served from Cloudflare's edge, with Sanity for editing.
Comes with discovery and AI value
Explore Fast, secure websites →
The problem
A content-managed site is a server with a plugin stack bolted on. It is the largest attack surface you own, the slowest thing your buyers touch, and a bill that grows on its own.
Why change
The cheapest move in the programme, and the first to pay back. The exposure goes, the speed Google ranks on improves, the hosting line falls — and nothing else has to change first.
Your current state
WordPress · Drupal · Joomla
New state
Astro · Cloudflare · Sanity
Objective value. No daily or weekly plugin updates and no test cycle after each one. Pages that load fast on the measures Google ranks on, and hosting at a fraction of a managed CMS.
Benefits
- No plugin treadmill: no weekly patch-and-retest cycle, no server, and no admin login to brute-force. Who benefits: IT & security, Board.
- Pages land fast on the measures Google ranks on — and fast enough that a buyer feels it. Who benefits: Marketing, Sales.
- Static files at the edge cost a fraction of a CMS server, with no plugin licences. Who benefits: Finance.
- Editors write in a system built for writing, not a page builder they fight. Who benefits: Marketing, Content.
- Content comes out structured, so an agent can read it and act on it. Who benefits: Data & AI.
- Astro and its dependencies still get upgraded — on a planned cadence we run and test once, not a scramble after every vendor release. Who benefits: IT & security, Delivery.
Outcome if left
- The next plugin vulnerability becomes a disclosure, not a maintenance ticket.
- Mobile speed keeps costing rankings and conversions nobody puts a number on.
- The weekly plugin-and-retest cycle keeps consuming time, and the maintenance line keeps rising.
- Every AI project starts by scraping your own website.
Technology
Website CMS. WordPress, Drupal, Joomla, Craft CMS, Webflow, Sitecore, Wix.
Ecommerce Platforms. Shopify, WooCommerce, Magento, BigCommerce, PrestaShop, OpenCart, Shopify Hydrogen, BigCommerce Catalyst, Medusa, commercetools.
Headless CMS. Sanity, Storyblok, Contentful, Strapi, Payload.
Web Frameworks. Astro, Next.js.
Cloud & Infrastructure. AWS, GCP, Azure, Cloudflare, Cloudflare Workers AI, DigitalOcean, Supabase, Docker, Terraform, Infisical.
Engineering, Testing & Monitoring. Sentry.io, Playwright, Puppeteer, GitHub.
Agentic leverage
Agents keep it current: content drafted into Sanity, speed watched, broken links and schema repaired, and every deploy checked against the live page before it counts.
Why us
One team does the whole move — strategy, build, data, automation and the operating system that keeps it improving. You are not assembling four suppliers to get one outcome, and the team that migrates the site is the team that runs it.
How you buy it
- Project (usual route). Fixed scope, fixed price. The site is rebuilt, proven against the old one, and handed over.
- Managed service (usual route). Hosting, content, search and conversion work run for you. Outcome-led, from £3,000 a month.
Discovery and value engineering
Project
We provide discovery and value engineering as a standalone service, and we also include it within our service modules. It is a critical value engineering stage of any project: defining the problems, the value, accountability, the stakeholders, the timelines, the risks, the process, the technology and the KPIs.
Explore Discovery and value engineering →
The problem
Most digital spend is committed before anyone has put a number on the return. Scope is agreed in a meeting, priorities follow whoever argued hardest, and the business case gets written after the decision.
Why change
A discovery costs a fraction of the build and changes what gets built. It is the only step that can stop you spending six figures on the wrong module.
Your current state
Proposals without numbers · Gut-feel priorities · Scope agreed in a meeting
New state
A costed value model · Ranked by payback · A plan you can sign
Objective value. You know what the work is worth before you commit to it — and what it costs you to do nothing.
Benefits
- A value model for each opportunity — what it creates, enhances or protects, and roughly how much. Who benefits: Board, CFO.
- Work ranked by payback rather than by who asked loudest. Who benefits: CEO, Leadership.
- A plan costed and sequenced, so procurement has something to price. Who benefits: Finance, Procurement.
- The measure agreed before the build starts, so success is not argued about afterwards. Who benefits: Leadership, Delivery.
Outcome if left
- Budget goes to the loudest voice rather than the largest return.
- Every project rebuilds its business case from scratch.
- You find out what a module was worth only after paying for it.
- Nothing is measurable afterwards, because nothing was measured first.
Technology
Discovery & Value Engineering. Google Forms, Google Sheets.
AI & LLM. OpenAI, Anthropic Claude, Perplexity.
Business Intelligence & Data Apps. Retool.
Agentic leverage
Our discovery agents do the legwork — the site, the stack, the market and the competitor read gathered in days rather than weeks — so the discovery spends its time on judgement instead of collection.
Why us
Discovery is where we prove the method before you buy the build. We run it as DMAIC (Define, Measure, Analyse, Improve, Control) where a process already exists, and DMADV (Define, Measure, Analyse, Design, Verify) where one has to be created, so the value model is produced by a method rather than an opinion. Every module that follows is priced against the value model it produces, and the team that writes it is the team that delivers.
How you buy it
- Project (usual route). A fixed-price discovery: the value model, the ranked plan and the measures, delivered in weeks.
- Managed service. Refreshed inside a managed engagement as the business changes.
Digital funnel
Project, then managed
Search, paid media, email, social and outreach run as one funnel, so the website has demand flowing into it and every stage is measured.
Comes with discovery and AI value
Explore Digital funnel →
The problem
Most businesses buy channels, not a funnel. Search, ads, email and outreach are bought separately, run by different people or agencies, and measured on different numbers. Budget goes out with no owner, and leads arrive with nobody accountable for what happens next.
Why change
A fast site with no demand flowing into it earns nothing. This is the module that fills it, and it is the one that pays the website back.
Your current state
Channels run in silos · Leads with no follow-up · Spend nobody can tie to revenue
New state
One measured funnel · Every lead worked · Spend tied to pipeline
Objective value. One owner, one dashboard, one number. You can see which channel produced the revenue, not only which produced the clicks.
Benefits
- Every channel planned, built and run by one team against one number, so nobody is optimising a metric that does not pay. Who benefits: Marketing, Sales.
- Leads are worked on a defined path rather than whenever somebody remembers, so fewer are wasted. Who benefits: Sales, Leadership.
- Attribution you can defend at board level: which channel produced pipeline, and what it cost to produce it. Who benefits: Board, CFO.
- Email and lifecycle work the list you already own, which is the cheapest demand available to you. Who benefits: Marketing, Finance.
Outcome if left
- Budget keeps going to the channel with the loudest agency rather than the best payback.
- Leads arrive and go cold, and nobody sees it because no single person owns the path.
- The website gets faster and better and still earns nothing, because nothing is being sent to it.
- Every reporting cycle restarts the argument about which number is right.
Technology
CRM & RevOps. HubSpot, Salesforce, Twenty CRM, Zoho, Dynamics 365, Apollo, Clay.
Marketing & Paid Media. Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads, Microsoft Ads.
Email & Lifecycle. Mailchimp, Klaviyo, Customer.io, SendGrid, Resend, Mailjet, Mailgun.
Agentic leverage
Agents take the repetitive half: audiences refreshed, copy drafted for a human to approve, spend watched daily rather than monthly, and a flag raised when a channel drifts before the month closes.
Why us
Discovery comes first and AI runs through it, as with every module. What is different here is that the funnel is wired into the same data layer as everything else, so the number in the marketing report is the number in the board pack.
How you buy it
- Project. A fixed-scope build: the funnel designed, the channels set up and the measurement wired in.
- Managed service (usual route). Planned, run and optimised month to month, reported against pipeline and revenue.
Market sizing, ICP and competitor analysis
Project
Four named analyses: TAM, SAM and SOM market sizing — the total, serviceable and obtainable market; ICP, the ideal customer profile; RFMP customer value analysis — recency, frequency, monetary value and profit; and competitor analysis that names who wins, where, and on what.
Comes with discovery and AI value
Explore Market sizing, ICP and competitor analysis →
The problem
Most growth plans rest on a market figure nobody can source and a profile too vague to act on. Sales then spends its most expensive hours on accounts that were never going to buy.
Why change
Everything downstream is priced off these numbers. Get them right first and a year of sales and marketing spend stops landing on the wrong accounts.
Your current state
Unsourced market size · Vague ICP · Competitors by anecdote
New state
Sized market · Filterable ICP · Named competitors
Objective value. A market you can defend to a board and a target list your sales team can work on Monday.
Benefits
- A market size you can defend to a board, built from data rather than a slide. Who benefits: Board, CEO.
- A profile specific enough to filter a prospect list by. Who benefits: Sales, Sales ops.
- Your existing customers ranked by profit, not just by spend, so retention money goes to the ones worth keeping. Who benefits: Finance, Sales.
- A competitor read that names who wins, where, and on what. Who benefits: Sales, Product.
- Campaign spend aimed only at accounts that match. Who benefits: Marketing, Finance.
Outcome if left
- Pipeline targets stay guesses, so forecasting stays a negotiation.
- Sales keeps working accounts that will not close, at full cost.
- You keep losing to a competitor nobody has studied.
- Budget spreads evenly across a market you have never sized.
Technology
AI & LLM. OpenAI, Anthropic Claude, Gemini, Perplexity.
Data Warehouse. BigQuery.
Business Intelligence & Data Apps. Power BI, Looker.
CRM & RevOps. HubSpot, Salesforce, Twenty CRM, Zoho, Dynamics 365, Apollo, Clay.
Agentic leverage
Agents rerun the sizing and the competitor read on a schedule, pushing newly matching accounts into the CRM as they appear. The list stays alive instead of ageing in a document.
Why us
We size the market and then build the systems that act on it. The same team that writes the profile wires it into the CRM and the campaigns. Customer value analysis needs your transaction history, and the profit dimension additionally needs cost to serve and discount data at customer level — so recency, frequency and monetary value is the baseline we always deliver, and profit is added where margin data exists.
How you buy it
- Project (usual route). A fixed-scope analysis delivered as a document and a filterable target list.
- Managed service. Refreshed each year as part of ongoing discovery for Enhance and Create clients, so the list stays current.
Data warehouse and a KPI dashboard per department
Project, then managed
One warehouse, one agreed definition of each number, and a board for each department.
Comes with discovery and AI value
Explore Data warehouse and a KPI dashboard per department →
The problem
The numbers live in five systems and disagree. Month-end is assembled by hand from exports, and by the time it lands the month it describes is over.
Why change
Nothing compounds until the numbers agree. Every dashboard, automation and agent downstream depends on one definition of revenue, pipeline and margin.
Your current state
Five systems disagree · Manual month-end · Exports on request
New state
One warehouse · Agreed definitions · A board per department
Objective value. One agreed number per measure, a board per department, and answers the same day instead of the next month.
Benefits
- One version of each number, so two meetings stop quoting different revenue. Who benefits: CFO, Board.
- Every department sees its own measures without asking for an export. Who benefits: Heads of department.
- Questions answered the same day instead of the following month. Who benefits: CEO, Operations.
- Finance stops rebuilding the same spreadsheet every month. Who benefits: Finance.
- The base agentic AI needs. An agent cannot act on numbers nobody agrees on. Who benefits: Data & AI.
Outcome if left
- Decisions keep being made from the newest spreadsheet in the room.
- Month-end stays manual, with one person as the single point of failure.
- Nobody can say which channel actually produced the revenue.
- Every automation and AI project stalls at “whose number is right”.
Technology
Automation & Integration. Make.com, n8n.
Data Warehouse. BigQuery, Snowflake, Databricks, Keboola.
Business Intelligence & Data Apps. Power BI, Tableau, Looker, Superset, Retool, Plotly, Streamlit, Airtable.
Cloud & Infrastructure. Supabase.
Agentic leverage
Agents own the definitions: pipelines monitored, a broken source flagged before the board pack is built, and questions answered from the warehouse in plain English.
Why us
We are not a reporting contractor. The team that agrees the definitions also owns the pipelines, the boards and the agents that watch them.
How you buy it
- Project (usual route). The warehouse built, the definitions agreed and signed off, the first boards live.
- Managed service (usual route). New sources, new measures and new boards added as the business changes.
Digital voice and conversational AI
Managed service
Every call answered, routed, transcribed and acted on, then written into the CRM.
Comes with discovery and AI value
Explore Digital voice and conversational AI →
The problem
Calls are still how the highest-value enquiries arrive, and the only channel with no queue, no log and no fallback when the team is busy.
Why change
Call volume is the one input that grows with success. Fix it before the growth arrives and the next hundred enquiries cost nothing extra to answer.
Your current state
Calls ring out · Notes in one head · Phone menus
New state
Answered day and night · Summaries in the CRM · A straight answer
Objective value. Every call answered, summarised and written into the CRM — without adding a head.
Benefits
- Nothing rings out, including evenings and weekends. Who benefits: Sales, Customer service.
- The caller gets a straight answer instead of a menu. Who benefits: Customers.
- Every call lands in the CRM already summarised and tagged. Who benefits: Sales ops.
- Call volume stops setting the size of the team. Who benefits: Finance, Operations.
Outcome if left
- Missed calls are lost revenue that never appears on a report.
- What was said on a call exists only in one person's memory.
- More enquiries means hiring, because capacity is people.
- Out-of-hours callers reach whoever answers first, which is usually a competitor.
Technology
Voice & Conversational AI. ElevenLabs, Deepgram, Twilio, Twilio Flex, Twilio Verify, Twilio Programmable Messaging, Twilio Voice, Twilio Functions, Twilio Studio, 3CX, Asterisk, Pipecat, Retell AI.
Agentic leverage
Here the agent is the delivery. Calls answered, intent captured, CRM written and follow-up raised, with no human in the loop on the routine path.
Why us
We run voice as part of a growth system, not as a phone project — so a call becomes a record, a task and a follow-up, not just an answered ring.
How you buy it
- Project. A single line or flow built and proven — usually a pilot before the estate moves.
- Managed service (usual route). The voice estate run, tuned and monitored. This is how it is normally bought.
Automation and integration
Project, then managed
The joins between your systems built once, monitored, and owned by us.
Comes with discovery and AI value
Explore Automation and integration →
The problem
Work moves between CRM, finance, delivery and inbox because a person copies it. That person is the integration, the bottleneck and the error rate.
Why change
Every new system multiplies the manual joins. Doing it now costs less than doing it after the next tool lands, and it breaks the link between volume and headcount.
Your current state
People re-keying · Silent failures · Undocumented joins
New state
Joins built once · Alerts on failure · Owned and documented
Objective value. Hand-offs move in seconds, failures raise an alert instead of going quiet, and volume stops driving headcount.
Benefits
- Re-keying stops, and the errors that come with it stop too. Who benefits: Operations.
- Work moves the moment it is ready, not the next morning. Who benefits: Delivery, Customers.
- A failed hand-off raises an alert instead of going quiet. Who benefits: Operations, IT.
- The joins are documented and owned, not lore in one person's head. Who benefits: Leadership.
Outcome if left
- Headcount grows with volume, because the joins are human.
- Errors are found by customers rather than by monitoring.
- Key process knowledge leaves the business when one person does.
- Every new system makes the copying problem worse, not better.
Technology
AI & LLM. LangGraph, Hermes.
Automation & Integration. Make.com, n8n, Zapier, DocuSeal, DocuSign, PandaDoc.
CRM & RevOps. HubSpot, Salesforce, Twenty CRM, Zoho, Dynamics 365, Stripe, Xero.
Project Management. Jira, Jira Service Management, ClickUp, Monday.com.
Email & Lifecycle. Mailchimp, Klaviyo, Customer.io, SendGrid, Resend, Mailjet, Mailgun.
Cloud & Infrastructure. AWS Lambda, Google Cloud Functions, Apache OpenWhisk.
Agentic leverage
Each join becomes an agent's responsibility, not a script's: it monitors itself, repairs a changed vendor API, and raises the exception instead of failing silently.
Why us
Complete digital transformation on one contract: the joins we build are owned, monitored and improved by the same operating system that runs everything else.
How you buy it
- Project (usual route). A defined set of joins built, tested and handed over.
- Managed service (usual route). Monitored, fixed when a vendor changes an API, and extended as you add systems.
AI — generative, RAG and agentic
Project to prove, managed to run
Generation for volume, retrieval-augmented generation for accuracy on your own documents, agents for work that runs without you.
Explore AI — generative, RAG and agentic →
The problem
Most AI pilots stall for two reasons: the model answers from general knowledge instead of your documents, and nothing connects it to the systems where the work happens. The demo impresses, then nothing changes.
Why change
The distance between a demo and a system is governance and integration. Close it now and pilot spend becomes a capability you keep rather than a write-off.
Your current state
Consumer tools, unlogged · Pilots that stay demos · General-knowledge answers
New state
Governed and logged · Into systems of record · Grounded in your documents
Objective value. Answers grounded in your own documents with the source shown, and volume work off senior desks.
Benefits
- Answers grounded in your own documents, with the source shown. Who benefits: All staff, Compliance.
- Volume work — drafts, summaries, classification — stops consuming senior time. Who benefits: Leadership, Marketing.
- Agents run defined work end to end, with a log of what they did. Who benefits: Operations.
- 59 of the 118 tools we run can be driven natively by an agent, so the work reaches your real systems. Who benefits: IT, Data & AI.
Outcome if left
- Staff use consumer AI tools on company data, unlogged and ungoverned.
- Pilots keep producing demos that never reach a system of record.
- Competitors compress the same work from days into hours.
- The cost of the work stays linear with the volume of it.
Technology
AI & LLM. OpenAI, GPT, Codex, Anthropic Claude, Cowork, Cursor, Gemini, OpenRouter, Kimi K3, DeepSeek, Grok, Perplexity, Vertex AI, LangGraph, Google ADK, Hermes, LangChain, LlamaIndex, Pinecone.
Cloud & Infrastructure. Cloudflare Workers AI.
Agentic leverage
This is where the agentic capability is built. The models here consume Model Context Protocol servers rather than exposing one, which is why the leverage shows up in the other six modules rather than in this card's own count.
Why us
We engineer value, not demos. Every use case is proven against a measure agreed up front, then handed to the operating system that keeps it running and costed.
How you buy it
- Project (usual route). One use case proven against a measure agreed before it starts.
- Managed service (usual route). The proven use cases run, monitored for drift and cost, and extended.
SAOS — the Sentry Agentic Operating System
Managed service
Named agents hold each standard, a control plane gives them the work and records what they did and what it cost.
Comes with discovery and AI value
Explore SAOS — the Sentry Agentic Operating System →
The problem
Digital projects finish, then decay. Nothing owns the standard after go-live, so the same defect returns and the next gain needs another project and another budget round.
Why change
Without an owner for the standard, everything above starts decaying the day it ships. This is what makes the other six keep paying long after the invoice.
Your current state
Projects that decay · Standards in people’s heads · Defects that recur
New state
Standards held by agents · Checks in the contract · Maturity that ratchets
Objective value. Every fix becomes a permanent check, so defects stop recurring and improvement carries on after go-live.
Benefits
- Maturity ratchets — a fix becomes a QA check written into the agent's contract, so the defect cannot return. Who benefits: Leadership, Quality.
- Improvement continues after go-live, which is where most digital programmes stop. Who benefits: Board.
- Work runs without adding headcount as volume grows. Who benefits: Finance.
- Every run leaves an audit trail: what was done, by which agent, at what cost. Who benefits: Compliance, Finance.
- Run as Define, Measure, Analyse, Improve, Control — the Control step is the part nobody else sells. Who benefits: Operations.
Outcome if left
- The same defects return, and each one is handled as though it were new.
- Standards live in people's heads and leave when they do.
- Every improvement needs a project, a business case and a budget round.
- You keep buying tools and never compound the value of the ones you have.
Technology
AI & LLM. OpenAI, Codex, Anthropic Claude, Cowork, Cursor, Perplexity, LangGraph, Google ADK, Hermes, LangChain.
Automation & Integration. Make.com, n8n, Zapier, DocuSeal.
Data Warehouse. BigQuery.
Business Intelligence & Data Apps. Power BI.
Project Management. Jira, Jira Service Management, ClickUp, Monday.com.
Cloud & Infrastructure. Cloudflare, Supabase, Docker, Terraform, Infisical.
Engineering, Testing & Monitoring. Sentry.io, GitHub.
Agentic leverage
SAOS is not bought separately. It arrives through whichever module you start with and compounds as you add the next, which is why every module is an entry point rather than a silo.
Why us
This one is ours. The Growth Engine is the offer, SAOS is the operating system behind it, and the team engineers rapid value instead of selling a transformation programme.
How you buy it
- Project. A maturity assessment and one agent put into live service, so you see the pattern working before committing to it.
- Managed service (usual route). The operating system run and matured around your business, month on month. This is how it is normally bought.
OpenAI 
Claude 
n8n 
Make.com 
BigQuery 
Snowflake 
Supabase 
Power BI 
HubSpot 
Salesforce 
Google Ads 
Meta 
Shopify 
WordPress 
Astro 
Cloudflare 
AWS 
GitHub