Industries
Five sectors, not a generic pitch.
Each one below gets its own systems, its own approval point, and its own proof — not a copy-pasted automation pitch with the industry name swapped out. These are the sectors where the work has already shipped.
Top Rated on Upwork — 22 clients, 31 contracts, 18 reviews
n8n, GPT-4o, Claude, AWS Bedrock, Microsoft Graph, Clio, LangGraph
Law firms
Attorneys and paralegals spend their mornings on mail that follows a pattern, and their afternoons assembling documents out of records that already exist somewhere in the system.
Legal work is high-volume, deadline-bound, and unforgiving of error — which is exactly why so much of it is still done by hand. The automation that survives in a firm is the kind that respects privilege, keeps an attorney in the loop, and fails loudly rather than quietly.
- Client, court, and counsel email triaged by matter and urgency
- Replies drafted against the matter's own history, never sent unreviewed
- Matter context pulled from Clio and equivalents
- Personal-injury demand letters assembled from the record set
- Precedent and policy retrieval over the firm's own files
The repetitive half of the inbox stops consuming billable attention, and document assembly starts from a populated draft instead of a blank page.
Marketing & creative agencies
The pipeline lives in three tools at once and the glue between them is a person. Status updates, handoffs and client reporting get written by whoever has least to do that afternoon.
An agency's operating system is usually a CRM, a project tool and a spreadsheet that quietly outranks both. The automatable part is the glue: the sync, the follow-up, the enrichment, the status update nobody has time to write.
- A single internal CRM replacing the per-tool sprawl
- Service sections, recurring deliverables and pipeline stages per client
- Meeting notes turned into CRM records and next actions
- Follow-up triggered by real pipeline events, not by a calendar
- Client-facing reporting generated from the records already there
One place where the state of every account is true, and a follow-up that happens because the pipeline moved rather than because someone remembered.
B2B SaaS teams
A beta launch is a wall of small blocking issues across four surfaces at once, and the team is already fully committed to the roadmap.
SaaS work here is full-stack delivery rather than workflow automation: dashboards, role hierarchies, billing surfaces and the AI features inside the product itself — built to a launch date and handed over documented.
- Multi-tenant dashboards with role-aware access
- AI features built into the product rather than bolted beside it
- Billing and tier logic wired to the subscription provider
- Type and build errors cleared ahead of a beta launch
- Handover documentation the in-house team can maintain
A launch that holds its date with nothing deferred to a later phase.
Proof here is the TeaganAI platform — four dashboards delivered at launch across three contracts, and the one client review on this site that describes a whole build.
Finance & accounting operations
Reconciliation, reporting and client updates depend on someone exporting a file on a schedule and pasting it somewhere else.
Finance operations are the clearest case for automation and the least forgiving of a wrong answer: the work is rule-shaped, high-frequency, and auditable or it is worthless. These systems are built to log every step and stop rather than guess.
- Scheduled data movement between finance and operations systems
- Document extraction from statements, invoices and reports
- Classification and routing with a confidence threshold
- Exception queues for anything the rules do not cover
- Run logs that make every automated decision auditable
The scheduled, rule-shaped half of the work runs itself, and the exceptions are the only thing left to look at.
Current engagement: automation engineering on Make.com for an AI and finance automation practice, running since September 2026. No published case study yet.
Professional services & consultancies
Expertise is trapped in documents and in the heads of two senior people, and every proposal starts from scratch because the last one is unfindable.
These businesses sell judgement, and the judgement is already written down — in proposals, decks, notes and process docs nobody can search. The work is to make that corpus answerable and to stop the same document being rebuilt every month.
- Internal assistants answering over the firm's own approved documents
- Proposals drafted from existing records and prior engagements
- Lead enrichment and routing before anyone reads an enquiry
- Onboarding packets generated from the source record
- Reporting assembled from the systems already in use
The firm's own written knowledge becomes answerable in seconds, and a proposal starts at a draft.
Also drawn from AI systems work for an online-course business and an agent build for a nonprofit.
Adjacent, and I will say so
The same system shapes fit IT services and managed service providers, recruiting and staffing, commercial contractors, dental and medical clinics, and HVAC companies — document-heavy intake, repetitive triage, and a CRM that is never current. I have not delivered in these sectors yet, so treat this as a statement of fit rather than of track record. The discovery call is where that gets tested, not the website. Tell me about the process.
For clinics specifically, that shape is missed calls and online inquiries going unanswered, intake paperwork copied by hand into the system of record, and recall or follow-up reminders that depend on someone remembering to send them. See what that looks like under Support inbox & voice agents, Document intake & extraction and CRM & client operations.
For HVAC companies specifically, that shape is service and quote follow-up falling through the cracks in a CRM nobody keeps current, calls and messages piling up during a busy job, and estimate or invoice paperwork copied by hand into the system of record. See what that looks like under CRM & client operations, Support inbox & voice agents, Document intake & extraction and Custom internal tools.
Losing hours to work an AI system could handle?
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