You have hit the wall already: the API with no Clay integration, the site that blocks the scraper, the CRM field the connector refuses to write to. Most people selling GTM automation stop right there, because they learned a tool rather than how to build one. This page is about the other side of that wall.
Each of these was built because a no-code tool could not do it. Each one replaced manual work or an integration that did not exist.
Exposes the ATS to Claude across candidates, jobs, applications, contacts, companies, notes and users. The team queries and updates their recruitment system in plain language instead of clicking through screens.
Replaced: dashboard clicking
Transcribes call recordings and gives natural-language retrieval of what was said on any call. Ask what a prospect objected to, get the answer, without scrubbing through audio.
Replaced: manual note taking
Scrapers that pull openings from platforms and career pages with no public feed, deduplicate them, and hand them to a classification layer. This data powers the recruitment pipeline.
Replaced: daily manual searching
Purpose-built internal apps that match candidates to roles and generate structured CVs from raw database records, cutting preparation time dramatically.
Replaced: hours of prep per role
A sync layer that keeps availability and events consistent across separate calendar accounts, so scheduling does not break when work spans more than one identity.
Replaced: double booking
Forwards replies from thirty plus secondary sending domains into one primary inbox, so a hot response is never missed regardless of which domain sent the original email.
Replaced: daily manual checking
The rarest part of the offer, and where the engineering background pays off most directly.
Model Context Protocol servers that plug Claude straight into a client CRM, call recordings and pipeline. The team queries all of it in natural language. Almost nobody in the GTM services market offers this.
Agentic coding ships pipelines and internal tools far faster than a traditional development cycle — the reason a solo practice delivers work that normally needs several people.
Reusable capability modules that encode a process so it runs identically every time, instead of depending on who happens to be at the keyboard.
Qualification, classification and personalisation built with structured outputs, evaluation and version control — behaviour that is measured, not assumed.
Schema enforcement, fallback handling, cost control, and evaluation against a labelled sample — the difference between a demo that looks great on ten rows and a pipeline you can point at your whole market and trust.
If a tool never built the integration you need, that is not the end of the project. The code gets written, so your roadmap is not set by someone else’s backlog.
The best buying signal is often in the place with no public API. Custom scraping and parsing reaches data that off-the-shelf tools cannot.
When a platform raises prices or removes a feature, a well built system routes around it, because the logic lives in code you own.
Accounts, domains, repositories and documentation all sit under your name. The build is on your balance sheet, not a subscription you rent.
More context on the about page; worked examples run on the blog.
In your accounts and your repositories. Everything Senko builds is provisioned under your ownership: the domains, the automation platforms, and the custom code. You keep the asset if the engagement ends.
A Model Context Protocol server connects an assistant like Claude directly to your own systems, so your team asks questions of your CRM, your call recordings, or your pipeline in plain language instead of clicking through dashboards. It turns your data into something you can talk to.
Yes. Builds ship with documentation, recorded walkthroughs, and readable code. The point is a system that survives a person leaving or a tool changing its pricing, not a black box only one contractor understands.
You get the accounts, the repositories, written runbooks, and a walkthrough of every moving part. Support continues for an agreed window so the handover is real rather than a file dump.
Every build. Schemas, triggers, failure modes, and how to change things are written down. Undocumented automation is a liability, not an asset.
By treating it as software, not a prompt: structured outputs with schema enforcement, fallback handling when a call fails, cost control on volume, and evaluation against a labelled sample so quality is measured rather than assumed.
An API with no connector, a source with no feed, an AI workflow that keeps breaking at scale — that is the conversation. Twenty minutes, and you leave knowing whether it is buildable.
Two build slots open this month · technical calls welcome