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REFERENCE ARCHITECTURES & DECISION FRAMEWORKS · AUSTIN, TX

Engineering decision frameworks and technical notes grounded in the systems available to inspect on this site.

Three pieces, each tied to a demo or case study you can open: bounded retrieval, coded workflow reliability, and review paths for automated data entry.

Read the three pieces

WHAT YOU’LL FIND

Reference architecturePrivate AI search with a refusal boundary, tied to the retrieval demo.Decision frameworkWhen a glue tool stops being enough, tied to the queue demo.Decision frameworkReview paths for automated data entry, tied to the MCP demo and case study.
DECISION FRAMEWORK

Build decision guide

Practical evaluation benchmarks for when to build custom automation vs. leveraging standard tools.

01 / UNRESOLVED QUERY

People cannot find answers in docs or tickets.

USEFUL FIRST BUILD

Grounded search with citations and filters.

WHEN TO AVOID CUSTOM WORK

If keyword search already gets the right answer fast.

WHAT TO VERIFY

Source quality, access rules, and update frequency.

02 / MANUAL TRIAGE

Leads, tickets, or invoices need manual triage.

USEFUL FIRST BUILD

Classification plus a human review path.

WHEN TO AVOID CUSTOM WORK

If the decision is rare or too subjective to codify.

WHAT TO VERIFY

False positives, exception handling, and rollback.

03 / UNSTABLE PIPELINE

A no-code workflow keeps breaking.

USEFUL FIRST BUILD

Small custom service with logs and retries.

WHEN TO AVOID CUSTOM WORK

If a standard integration handles the edge cases.

WHAT TO VERIFY

Maintenance owner, credentials, and failure alerts.

04 / NEW PRODUCT MVP

A founder needs a first product, not an internal workflow.

USEFUL FIRST BUILD

Scoped MVP with auth, database, and the core user path.

WHEN TO AVOID CUSTOM WORK

If a landing page, prototype, or manual concierge flow can validate first.

WHAT TO VERIFY

Must-have workflow, launch constraint, and ownership path.

What makes AI useful in workflow systems

The model is only one part. Useful systems need clean inputs, clear decisions, and a way for humans to review uncertain work.

  • Start with one repeated workflow.
  • Keep source records visible.
  • Route unclear cases to a person.

What makes AI search trustworthy

Search should point back to real documents, tickets, records, or product data. If the source is weak, the answer will be weak too.

  • Clear ingestion and update path.
  • Filters for facts like customer, date, status, or product.
  • Citations or record links wherever possible.
PRODUCTION CONSIDERATIONS

What it takes to run in production

Storage and retrieval

Choose embedded search, hosted vector search, or keyword/hybrid retrieval based on data size, latency needs, and maintenance burden.

Growth path

Start with the smallest reliable architecture, then add replicas, queues, caching, or dedicated search infrastructure when usage actually demands it.

Integrity Protocols

Hybrid retrieval (keyword plus embeddings), cited sources, and a refusal when the answer is not in the corpus, as shown in the retrieval demo.

DATA HANDLING

Data isolation

Use separate environments and indexes when data sensitivity or client boundaries require it.

No training

Choose providers and settings that keep business data out of generic model training.

Grounded answers

RAG/search outputs should cite records, docs, tickets, or source systems where possible.

Exit path

Clients should understand where indexes, source data, and system metadata live.

INSIGHTS

Reference architectures and decision frameworks

Engineering decision frameworks and technical notes grounded in the systems available to inspect on this site.

Insight articles

INSIGHTS (3) UPDATED: 2026-09-11
Reference architecture
A private AI search workflow for internal operations
Read insight
Decision framework
Zapier vs. custom code: when a glue tool stops being enough
Read insight
Decision framework
Data entry automation should have a review path
Read insight
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