[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_108":109,"_110":111},"routes/embed.x.$handle.$name.$",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":22},"handle","guardianengr","name","douglas-mustapick-ai-consulting","path","index.html","project",{"_23":45,"_11":12,"_34":46,"_47":-5,"_48":42,"_49":50,"_51":50,"_52":53,"_54":-5,"_55":56,"_57":58,"_59":33,"_60":-5,"_61":-5,"_62":63,"_64":65,"_66":-5,"_67":-5,"_68":-5,"_69":-5,"_70":71,"_72":71,"_73":74,"_75":74,"_76":74,"_77":71,"_78":71,"_79":80,"_81":82,"_83":84,"_85":42},"file",{"_23":24,"_11":14,"_25":26,"_27":-5,"_28":29,"_30":31,"_32":33,"_34":35,"_36":37,"_38":17,"_39":40,"_41":42,"_43":-5,"_13":44},"permalink","https://www.val.town/x/guardianengr/douglas-mustapick-ai-consulting/code/index.html","extension","html","id","0ffdb908-487d-11f1-ad83-42b51c65c3df","fileId","019df815-c0e1-76d8-bd1b-f371552ecd32","parentId","branchId","e53d0d68-487c-11f1-85db-42b51c65c3df","branchVersion",2,"latest",true,"createdAt",["D",1777983740144],"version",1,"fileType","content","\n\n \n Douglas Mustapick — AI-Native Prototyping & Consulting\n \n \n \n \n\n \n \n\n \n \n \n \n \n \n\n \n \n \n \n\n \n

\n DM.\n

\n \n \n Get in touch\n \n

\n\n \n

\n AI-Native Prototyping / Consulting\n

\n Douglas Mustapick.\n Low-code technical founder.\n

\n Builds working prototypes in weeks, not quarters.\n

\n\n

\n Claude Code\n Val Town\n Supabase\n Webhook-first\n Litigation-grade\n

\n\n \n

§ 01

What I'm Actually Selling

\n I don't sell finished software. I sell the ability to go from \"we have an idea\" to \"we have a working prototype your team can actually use\" — fast.\n

\n My stack is deliberately unconventional: Val Town for the HTTP and automation layer, Supabase as system of record, Claude Code for AI-assisted development. Production-adjacent prototypes at a speed traditional dev shops can't match — with enough architectural discipline that the prototype doesn't get thrown away when it's time to scale.\n

\n\n

\n →\n
Prototypes, not products. Working versions in weeks.
\n
\n →\n
Architecture, not just automation. Systems that can graduate.
\n
\n →\n
AI-native speed at low-code prices. Solo, not a dev team.
\n
\n →\n
Translator. Founder vision → technical reality.
\n

\n\n \n

§ 02

Use Cases I've Prototyped

Eight patterns I've shipped. Each was a working prototype in production-adjacent shape within weeks.

\n\n

01 / SIGNAL

Signal monitoring & OSINT pipelines

Multi-source early-warning systems — government SFTP feeds, public APIs, portal scrapes — piped into a Supabase timeline with alerting. Detect activity before it hits the news.

\n\n

02 / ENRICHMENT

Lead enrichment waterfalls

Pre-enrichment pipelines that take a raw address or company name and return a fully decorated record: licenses, geocode, risk data, jurisdictions, contacts, validated emails and phones.

\n\n

03 / VOICE

AI call & conversation analysis

Webhook handlers that ingest call recordings and transcripts, run structured analysis (objections, next steps, sentiment, tasks), and write results back to the CRM as structured fields.

\n\n

04 / DOCS

Document generation & compliance

PDF generation pipelines for regulated forms — affidavits, approval stamps, jurisdictional submittals — with append-only audit trails and litigation-grade data integrity.

\n\n

05 / ARCHITECTURE

CRM-as-projection-layer architecture

CRM becomes a projection over a canonical Supabase database via an Inbox/Worker/Reconciler pattern. Keep the visual tools operators love without trapping data inside them.

\n\n

06 / HUMAN-IN-LOOP

AI pre-scan + human-in-the-loop + QA

First-pass AI on structured documents, human corrects, corrections captured as training signal. Domain-specific AI that gets measurably smarter on your data over time.

\n\n

07 / OUTBOUND

LinkedIn authority & outbound engines

Full stacks combining ICP research, content strategy, and outbound automation for founder-led businesses. Relationship-driven pipelines that don't feel automated.

\n\n

08 / SCRAPING

Scraping & portal intelligence

Structured extraction from Accela, Tyler Technologies, Sunbiz, and similar government portals. Public records become proprietary business intelligence.

\n\n

\n\n \n

§ 03

How I Work

Three phases. Roughly three to six weeks end to end. You get something working — fast — and you decide what happens next.

\n\n

01 / DISCOVERY

Map the workflow

Find the actual pain. Define what a working prototype looks like.

\n\n

02 / SPRINT

Two-week build

Ship a working version. You use it. We find what breaks.

\n\n

03 / HANDOFF

Iterate or transfer

Keep refining — or hand over documented code and architecture.

\n\n

\n\n \n

§ 04

Fit Check

Honesty up front beats a bad engagement.

\n\n

✓ Best for

\n ›\n Founders who want to see it before scaling\n
\n ›\n Teams evaluating AI workflows\n
\n ›\n Companies tired of 6-month / 6-figure dev cycles\n

\n\n

✕ Not a fit

\n ›\n Day-one production hardening\n
\n ›\n SOC 2 out of the gate\n
\n ›\n Ten-engineer scopes\n

\n\n \n

\n Have an idea? Let's prototype it.\n

\n \n Email Douglas →\n \n \n LinkedIn\n \n

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