Services · AI Workflows
AI that does a specific job in your workflow.
I build LLM integrations into real operational workflows — document generation, data translation, content drafting, classification. This is not AI strategy or a pilot program; it's one scoped use case, built and tested against your actual data, delivered as working software. Designed for EDs, COOs, and operations leads who need a workflow problem solved, not a technology demo.
One use case, built and validated. Then the next one.
Who this is for
Organizations with a specific, repeatable workflow that costs staff hours every week.
- Nonprofit program directors whose bilingual staff spend hours writing English case notes for English-speaking supervisors
- EDs who produce board report narratives manually from QuickBooks or financial exports every quarter
- Development staff drafting grant applications from scratch when prior successful submissions exist
- Operations leads at organizations that receive intake form responses and spend time routing or re-entering them by hand
- COOs managing multi-program organizations who need summaries extracted from long compliance reports or funder documents
- Marketing and communications staff who generate social media content or email drafts from existing program data and newsletters
What I build
Specific integrations, not AI features.
Document generation workflows
Grant application first drafts generated from prior successful submissions and current program data — for staff review and editing before submission. Board report narratives drafted from QuickBooks or financial exports — reviewed by staff before use. The output is a starting point, not a finished document.
Data translation and transformation
Case note translation from Spanish to English for English-speaking supervisors reading bilingual staff records. LLM-based transformation between system data formats — converting structured data from one system's schema into another's without manual re-entry or custom ETL pipelines.
Classification and routing
Intake form responses classified and routed to the right program, staff member, or queue based on content — without manual triage. Document and record classification for organizations processing high volumes of similar inputs. Rules are transparent and auditable.
Content drafting from structured data
Social media post drafts generated from program data, newsletters, or event records. Email drafts assembled from structured inputs — for staff review before sending. Summaries extracted from long reports or funding documents, surfacing the key points staff actually need.
How this is different
Every engagement starts with one use case — the specific workflow that costs the most staff time — scoped, built, and validated against real data before anything else is discussed. The integration is built into your existing systems, not a standalone tool that creates a parallel process. AI outputs that staff act on always include a human review step by design; that is part of the scope, not an afterthought. Fixed scope and fixed price mean you know exactly what you're getting before work begins.
How it works
Fixed scope. Fixed price. One use case at a time.
- 01
Identify the use case
We find the one workflow that costs the most staff time and is repeatable enough to automate well. I map the inputs, the expected outputs, and what good looks like — documented in writing before any code is written.
- 02
Build and validate
One scoped integration, built and tested against your real data. Staff review the outputs before the workflow goes live. The integration connects to your existing systems — no new platforms, no parallel process.
- 03
Expand or hand off
Once the first use case is running and validated, you choose: extend to the next use case, or take a fully documented handoff. The system is yours — code, documentation, and all.
Book a 30-minute technical audit.
Bring the workflow that's costing your team the most time. We'll look at what the inputs are, what a validated output would need to look like, and whether an LLM integration is actually the right tool for it. No AI pitch. Just honest scoping of one specific problem.