AI · AI Consulting
AI Consulting & Strategy
AI consulting is senior-engineer advisory that decides where AI fits before anyone writes code. We audit candidate workflows across your product and operations, score each by impact and feasibility, and map use cases, data, model strategy, and build-versus-buy into one defensible plan. You leave with a scored opportunity map and an architecture sketch for the lead build candidate. We are engineers, not slide-deck consultants. Founder-led scoping, India + global delivery.
In short
What is AI Consulting & Strategy?
AI consulting is advisory work that decides where AI fits in a product or operation before engineering begins. Metaborong's engagement audits candidate workflows, scores each by impact and feasibility, maps data and model strategy, and makes the build-versus-buy call. You leave with a scored opportunity map and an architecture sketch for the lead build. Founder-led, India with global delivery.
What we deliver
Concrete artefacts, not capabilities
- 01
Scored opportunity map - every candidate workflow ranked by impact and feasibility
- 02
AI use-case shortlist with data and model strategy per candidate
- 03
Build-versus-buy recommendation for each shortlisted workflow
- 04
Architecture sketch for the lead build candidate
- 05
Risk register covering data, compliance, and integration constraints
How we work
Engagement phases
Discovery and workflow inventory
We run focused sessions with each team that owns a candidate workflow - support, sales, operations, product, engineering. Every workflow is captured with its current volume, manual hours, error rate, and data dependencies. The output is a flat inventory, deliberately exhaustive at this stage, before any scoring or filtering happens.
Use-case mapping and feasibility
Each workflow is scored against four axes: business impact, data readiness, integration cost, and regulatory exposure. Scoring uses your production constraints, not benchmarks - we test whether your data can actually ground a retrieval system before recommending one. Low-feasibility ideas are flagged early so the shortlist stays defensible to engineering and finance.
Data and model strategy
For each shortlisted use case we decide the approach: which foundation model, retrieval versus fine-tuning, and what data has to be in place to ground it. We make the build-versus-buy call explicitly, comparing an off-the-shelf tool against a custom build on cost, control, and time to value.
Recommendation and handoff
We present findings to engineering, product, and founders in a single working session, so disagreement surfaces before commitments harden. The deliverable is a decision document, not a recommendation deck - every shortlisted workflow has an owner, a budget, and a next step. For the lead candidate we hand off an architecture sketch so the build can start immediately.
Tech stack
What we build on
- LinearPlanning
- NotionKnowledge base
- MiroWorkflow mapping
- PythonFeasibility probes
- OpenAIModel probes
- AnthropicModel probes
- PostgreSQLData inventory
- Looker StudioImpact dashboards
- LinearPlanning
- NotionKnowledge base
- MiroWorkflow mapping
- PythonFeasibility probes
- OpenAIModel probes
- AnthropicModel probes
- PostgreSQLData inventory
- Looker StudioImpact dashboards
Scope
When this fits and when it doesn't
| This fits when | This doesn't fit when |
|---|---|
| You have a working product or operation and want to know where AI realistically helps. | You already know exactly what to build and only need engineering capacity to ship it. |
| You need a defensible, feasibility-tested plan before committing budget or roadmap. | You expect a deck of generic AI use cases - we ship a feasibility-tested plan instead. |
| You are weighing build-versus-buy and want an engineer read, not a vendor pitch. | You want a research engagement on novel model training - we integrate existing foundation models. |
Related work
Shipped engagements
- Live project
Mid-market SaaS - AI consulting and opportunity audit
Inventoried candidate workflows across support and sales, scored against feasibility, and shipped a sequenced adoption plan with a lead-candidate architecture sketch.
View live project - Live project
Retail BI deployment - data-readiness scoring
Audited the warehouse layer for AI-grounding readiness before recommending retrieval and copilot workflows for the operations team.
View live project
Frequently asked questions
AI consulting at Metaborong covers the decision before the build: a workflow audit, use-case shortlisting, data and model strategy, and a build-versus-buy recommendation. Deep delivery planning, sequencing, and governance live in our AI Adoption Roadmap engagement. Most buyers start here to learn where AI fits, then move straight into a build.
AI consulting decides what to build and whether to build it; AI development builds it. Our consulting engagement ends with a feasibility-tested shortlist and an architecture sketch for the lead candidate. If you already know what you want and only need engineering capacity, you can skip consulting and start with a build engagement directly.
Read-only sampled access to representative datasets, not full production. We score workflows against the schema and a representative slice - enough to test retrieval, embeddings, and routing without taking on production risk. Where security review is required first, we work behind an NDA inside whatever data-room arrangement your compliance team prefers.
Consulting engagements run one to two weeks of senior engineering with one founder leading. We scope fixed-bid based on the number of teams and candidate workflows. Most engagements include the architecture sketch for the lead candidate at no additional cost, so the build can start without a second scoping round.
One of three Metaborong founders runs every engagement, supported by the engineer who will own the eventual build. We do not hand consulting to junior associates - the people writing the production code are the people in the discovery room. India + global delivery, with timezone overlap arranged around the buyer.
Tell us what you are building.
We build what large agencies under-deliver and freelancers can't architect, across Web3 protocols, AI agents, and SaaS products. Tell us what you are building. We will tell you how we would approach it, no pitch deck, no fluff, no commitment required.