All projects

Technology Assessment / Workflow automation · Field operations

From field notes to a useful next step.

This work brings technology consulting into a repeatable workflow: capture what is actually present, interpret the evidence, and give the operator a plan they can review and explain.

Internal workflow in development · Built by Kevin Manner

The situation

An office assessment gathers uneven information about devices, backup, software, and infrastructure. Turning those observations into a consistent plan takes judgment. Missing information must stay visible, and related findings should not create duplicate work or costs.

A concrete example of turning collected observations into consistent, reviewable findings without asking AI to decide every result.

One concrete example

Keep missing evidence visible in a report.

  1. Input

    A synthetic device assessment records that a backup has not been completed and that the disaster recovery plan is unknown, alongside other device observations.

  2. What happens

    Explicit rules separate confirmed problems from missing evidence, rank the findings, and group related work so one response is not counted several times.

  3. Observable result

    The checked fixture produces 10 response items and 3 evidence gaps, grouped into 7 recommended actions. Completing and verifying the backup appears first; the unknown recovery plan remains an evidence gap.

  4. Human review

    The assessor compares the findings with the field notes and verifies unknowns before relying on the report. The tool structures that review; the assessor remains responsible for it.

This is a synthetic rule-evaluation example from the internal application, not a delivered client report. Draft resumption and a real approved-report export remain unfinished.

Synthetic assessment example

  1. 01 / ObserveRecovery plan: unknown.
  2. 02 / EvaluateFlag the missing evidence.
  3. 03 / ReviewVerify against the field notes.
A decision path from the synthetic assessment fixture. This shows the rule behavior, using no client data.

What I built

I translated experience from my technology consulting practice into assessment criteria, report logic, and workflow prototypes. I connected the earlier automation across WordPress, Make, Airtable, and Claude, then developed the current application with AI-assisted development around explicit records, deterministic evaluation, and human review.

Where it stands

The current application remains an internal workflow in development. Resuming drafts and producing a real export from an approved report still need completion and end-to-end verification. It is not a public client portal. The earlier PDF workflow should not be mistaken for an export feature in the current application.

The next useful improvement

Complete one synthetic assessment from draft through review, approved report export, and recovery after a restart. Keep the public service explanation separate from the operator application.

Technical decisions, workflow, and verification

Next.js / TypeScript · SQLite / Drizzle · Deterministic rules · WordPress / Make / Airtable (earlier workflow)

Follow the workflow

  1. Earlier workflow: connect the tools

    A WordPress assessment form fed Make and Airtable. Formulas structured report text and tiered pricing, and Claude helped generate an executive PDF debrief. This was an internal integration prototype.

  2. Current application: preserve the observations

    The Next.js application stores clients, sites, assessments, assets, and observations in SQLite. Evidence belongs to a record rather than only to an automation run.

  3. Evaluate before writing the narrative

    Deterministic rules distinguish strengths, response items, missing evidence, and optional upgrades. Related responses are grouped for sequencing and cost calculations.

  4. Review the proposed plan

    Report generation and approval versions preserve the review process. An optional AI-written executive debrief has a deterministic fallback; the operator still checks the findings against field notes.

Decisions and tradeoffs

Unknown is a useful answer

An unverified backup becomes an evidence gap, not a confident claim that backup is present or absent. This preserves a concrete task for the assessor to follow up.

Rules own the evaluation

Priorities, finding categories, and grouped costs come from explicit application logic. AI can assist the written debrief without being the sole source of the assessment’s conclusions.

Make the workflow inspectable

Moving from a chain of services to versioned application code and persisted records makes state easier to inspect. It also introduces responsibility for access, migrations, recovery, and completing the operator journey.

Explore the evidence

An implemented evaluation model

Existing fixture checks cover an all-positive device assessment, mixed findings with missing evidence and grouped costs, and an absent optional asset. They provide focused checks of the rules rather than proof of the complete service.

A distinct application generation

The current application implements SQLite persistence, deterministic findings, report generation, and approval records. These are separate from the earlier WordPress-to-PDF integration.

A concrete consulting use case

The public assessment page explains the service and provides a path to discuss a real office-technology problem.

Read about the assessment service