AI architecture advisory

AI architecture advisory for teams that need a clear build path, not another vague AI strategy conversation

Hire CraftingData when the AI initiative is important enough to need senior technical judgment but still uncertain enough that a wrong platform, orchestration, or delivery choice will cost real time and money. The work is scoped to produce fast clarity, practical recommendations, and a concrete path the team can execute.

  • Clarify architecture, platform, and vendor choices before committing
  • Pressure-test agent, data, security, and operating model assumptions
  • Turn prototypes or internal tools into a production-minded plan
  • Leave the team with decisions, artifacts, and next steps it can use
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Who this is for

Bring this in when the initiative is moving, but the architecture still feels unresolved

Prospective buyers usually do not need more AI enthusiasm. They need help answering practical questions: what should be built, what should not, which parts need custom architecture, how secure the operating model needs to be, and what sequence gives the team the best chance of shipping something supportable.

  • Useful when leadership needs technical tradeoffs explained clearly
  • Useful when engineering needs a production path, not just a demo
  • Useful when product needs help scoping a credible first version

What you get

A good engagement should leave you with decisions and artifacts, not just opinions

Written architecture recommendations and tradeoffs Clear next-step plan for engineering and product Assessment of security, data, and operating model gaps Decision support on platform, orchestration, and vendor choices Production-minded guidance instead of generic AI strategy decks Short, senior engagements with a defined outcome

What you can hire for

The work is strongest where architecture decisions shape delivery risk

Many AI initiatives fail in the gap between interesting prototype and supportable system. The job here is to close that gap: diagnose the architecture, identify what matters most, and turn ambiguity into an execution path the team can use.

Assessment

Architecture assessment before the team scales the wrong thing

Review agent patterns, orchestration choices, security model, model integration path, and operational assumptions before a pilot turns into technical debt with extra budget attached.

Planning

Prototype-to-production planning that engineering can actually use

Define what needs to change in architecture, interfaces, infrastructure, observability, and operating model so the team can move from promising demo to credible system.

Recovery

Recovery support for initiatives that are stuck or drifting

Step into AI work that is overcomplicated, underspecified, or no longer trusted by the team, then identify the shortest path back to a workable technical direction.

Why trust the technical judgment

Public coding work gives buyers something concrete to inspect before they commit

The repositories are not meant to mirror your domain. They exist as inspectable proof of working style: analytical rigor, clean package boundaries, APIs, documentation, testing, and the habit of turning ideas into software another person can actually run.

Repository one

portfolio-management

Open repository

An open-source portfolio optimization service built with FastAPI, PySCIPOpt, and SciPy. The project mirrors major public model families from the Gurobi Finance notebook collection, but uses open-source solver infrastructure instead of reproducing the original commercial setup.

Python FastAPI PySCIPOpt SciPy Azure Key Vault

What it covers

  • Core Markowitz-style allocation models and ranking logic
  • Leverage, turnover, transaction cost, and market-impact variants
  • Sector caps, factor models, cardinality, and round-lot constraints
  • Market data retrieval and estimator choices exposed through an API

Why a buyer should care

  • Shows translation from published quantitative ideas into maintainable Python code
  • Demonstrates API design, estimation choices, optimization modeling, and testing
  • Shows that the advisory work is backed by direct implementation capability
  • Gives technical buyers evidence beyond claims on a services page

Repository two

roth-conversions

Open repository

A retirement and Roth conversion analysis toolkit that evolved beyond notebooks into a library and CLI. It is designed to run scenario analyses, generate reports, and make tax-aware planning more inspectable and repeatable.

Python CLI Scenario configs Markdown/PDF reports Unit tests

What it covers

  • Three-path retirement comparisons and bracket-specific conversion scenarios
  • Config-driven analysis for repeatable what-if planning
  • Report generation for reviewable client-facing outputs
  • Refactoring from notebook-heavy work into a library-style package

Why a buyer should care

  • Shows how analytical logic can be turned into a library, CLI, and report workflow
  • Demonstrates refactoring discipline beyond notebook-only work
  • Supports a credibility story around turning analytical logic into usable software
  • Shows implementation follow-through instead of strategy-only thinking

How the work is done

Buyers should expect practical collaboration, not abstract consulting theater

Clarity

Start from the decision that matters most

The first step is not to expand scope. It is to identify the highest-value technical question blocking progress and make that decision explicit so the engagement does not dissolve into a general AI discussion.

Artifacts

Leave behind artifacts the team can keep using

A useful engagement ends with reviewable outputs: architecture recommendations, tradeoff notes, sequence plans, risk lists, interface suggestions, or implementation guidance that survives after the meetings are over.

Pragmatism

Bias toward the shortest credible path to production value

The goal is not architectural purity. The goal is to help the team make strong decisions, avoid expensive mistakes, and reach a supportable result without overbuilding.

Engagement options

The easiest way to buy this work is to start with a narrow scope and a defined outcome

Most buyers do not need a long commitment on day one. A short fit call followed by a fixed-scope assessment or planning sprint is usually enough to determine whether ongoing advisory support is justified.

First step

30-minute fit call

A short conversation to clarify the initiative, understand the architectural uncertainty, and decide whether a paid assessment or planning sprint is the right next move.

Fixed-fee entry offer

AI architecture assessment

A focused review of the current initiative covering architecture, model and agent integration choices, delivery risks, operating gaps, and the decisions that matter most next.

Fixed-fee planning

AI strategy and architecture roadmap

A focused engagement to prioritize the right use cases, evaluate technical and operational constraints, and leave with a delivery roadmap tied to an architecture path.

Ongoing support

Advisory retainer when needed

Ongoing senior architecture support for teams that need a trusted technical counterpart for reviews, design decisions, course corrections, and prototype-to-production guidance.