Primary research for the answer-engine era, our most-cited piece.
Five constraint numbers locked before build. Six stages from discovery to hand-off.
Resourcifi is a generative AI consulting company that turns ambition into a working system. We run a readiness assessment, prioritize use cases by value and feasibility, set governance, and stand up a proof of concept engineered to reach production. The same team that advises you also builds it, so the roadmap is costed against what we can actually ship.
Generative AI consulting helps an organization decide where AI creates value, then plan and de-risk the path to a production system. A typical engagement covers a readiness assessment of your data, skills, and infrastructure; prioritization of candidate use cases by business value and technical feasibility; a target architecture and model strategy; a governance and risk plan; and a costed roadmap. Many engagements also include a proof of concept so leaders can see results before committing to a full build. The gap it closes is real: McKinsey’s State of AI (2025) found 71 percent of organizations now regularly use generative AI in at least one function, up from 65 percent a year earlier, yet only about one third have scaled it. Most value is lost between the pilot and production.
Resourcifi runs these engagements with practitioners, not pure strategists. Founded in 2017 with more than 200 in-house experts, we bring the people who design retrieval pipelines, evaluate models, and run MLOps into the advisory room. That keeps the roadmap honest: scope, latency, and cost are estimated by the team that will deliver them, and the proof of concept is built on the same stack you would run in production.
Canon numbers from work delivered since 2017, not projections.
In our experience, AI initiatives stall less from weak models and more from weak grounding: no clear use case, no view of data readiness, no governance, and a proof of concept built on a throwaway stack that cannot survive contact with production. Our consulting closes that gap by costing every recommendation against a real build path.
Strategy meets implementation in one engagement
How we close the gap →We audit your data, infrastructure, skills, and existing tooling, then score readiness against each candidate use case so you invest where the foundation already supports it.
We run structured workshops to surface candidate use cases, then rank them on business value against technical feasibility so the roadmap leads with quick, defensible wins.
We design the reference architecture and choose between frontier models from OpenAI, Anthropic, and Google or open-weight Llama and Mistral for on-prem, based on data sensitivity, latency, and cost.
We set a governance baseline covering access control, PII handling, evaluation, human oversight, and an audit trail, engineered to the standard your regulators and customers expect.
We build a working proof of concept on your real data and the same stack you would run in production, with measured accuracy and latency rather than a demo on synthetic inputs.
We hand over a sequenced roadmap with scope, team shape, timeline, and cost for each phase, estimated by the engineers who would deliver it so the plan is buildable, not aspirational.
A short, structured path from question to working proof, with a feasibility check before any build commitment.
A worked example of how a single priority use case moves from question to evidence inside one engagement.
See the method →Illustration of how this works in practice, under guardrails and human checkpoints.
We stay tool-agnostic and recommend what fits your data, latency, and budget. Common building blocks across our engagements include the following.
Leadership wants AI but lacks a prioritized plan. We assess readiness, rank use cases, and deliver a costed roadmap with a proof of concept so the first phase is funded on evidence.
A demo works but cannot scale or pass review. We diagnose the data, architecture, and governance gaps, then re-platform it onto a stack engineered to reach production.
Healthcare, fintech, and legal teams need AI that respects access control, PII handling, and auditability. We set a governance baseline and an architecture engineered to the standard your reviewers expect.
The same operating discipline runs every build: the numbers locked before we start, an eval suite that has to pass, quality gates on every change, and a hand-off engineered from day one.
Read the full method →Align on goals, constraints, and stakeholders, then run discovery workshops to surface candidate use cases and map data and systems.
Audit data, infrastructure, skills, and tooling, and score readiness against each candidate use case.
Rank use cases on business value against technical feasibility and agree the sequence with stakeholders.
Design the target architecture and model strategy and set the governance and risk baseline.
Build a working proof of concept on real data with measured accuracy and latency on the production stack.
Deliver a costed, sequenced roadmap with scope, team shape, and timeline, estimated by the delivery engineers.
Pick the scope that fits where you are, from a focused assessment to a full advisory plus build partnership.
A focused engagement that audits readiness, prioritizes use cases, and delivers a costed roadmap, typically over a few weeks.
Everything in the sprint, plus a working proof of concept on your real data and production stack so leaders can decide with evidence.
Ongoing advisory with our engineers embedded to deliver the roadmap, with onshore-quality work at a fraction of typical onshore cost.
Tell us your use case and we will scope the right engagement. Or hire AI engineers for your own roadmap.
Answered the way we would on a scoping call.
AI consulting services help an organization decide where AI creates value and plan a de-risked path to production. A typical engagement includes a readiness assessment of data and infrastructure, prioritization of use cases by value and feasibility, a target architecture, a governance plan, and a costed roadmap. Many engagements also include a proof of concept built on real data.
Resourcifi runs engagements with practitioners, not pure strategists. The engineers who design retrieval pipelines, evaluate models, and run MLOps sit in the advisory room, so scope, latency, and cost are estimated by the team that will deliver them. You leave with a buildable roadmap and, in most engagements, a working proof of concept rather than slides alone.
A focused readiness and roadmap sprint typically runs a few weeks. Engagements that include a proof of concept usually run about six weeks: discovery and assessment in the first two weeks, architecture and governance next, then a proof of concept and a costed roadmap. Timelines depend on data access and the number of use cases in scope.
You receive a readiness assessment, a prioritized list of use cases ranked on value and feasibility, a target architecture and model strategy, a governance and risk baseline, and a costed, sequenced delivery roadmap. Engagements that include a proof of concept also deliver working code on your real data with measured accuracy and latency.
We stay tool-agnostic and recommend what fits your data sensitivity, latency, and budget. Engagements commonly use frontier models from OpenAI, Anthropic, and Google, or open-weight Llama and Mistral for on-prem needs, with LlamaIndex for retrieval, LangGraph for orchestration, MLflow and LangSmith for evaluation, and vector stores such as Pinecone or pgvector.
We run structured workshops to surface candidate use cases, then score each one on business value against technical feasibility. Feasibility accounts for data readiness, integration effort, latency, and cost. The result is a ranked sequence that leads with high-value, low-risk wins so early phases build momentum and fund the next stage.
We set a governance baseline covering access control, PII handling, evaluation, human oversight, and an audit trail, engineered to the standard your regulators and customers expect. For regulated sectors such as healthcare, fintech, and legal, we design the architecture and data flows around those controls from the start rather than adding them later.
Yes. We build proofs of concept on the same stack you would run in production, then deliver a costed roadmap to scale them. For stalled pilots, we diagnose the data, architecture, and governance gaps and re-platform the work so it can pass review and operate reliably under real load.
Cost depends on scope, from a focused readiness sprint to an advisory plus build partnership. Because every recommendation is costed by the engineers who would deliver it, you get a realistic figure rather than an open-ended estimate. Delivery work runs at a fraction of typical onshore cost while keeping onshore-quality standards.
Yes. With more than 200 in-house experts and over 600 projects delivered since 2017, the same team that advises you can implement the roadmap. This keeps the plan honest, because the people estimating scope and cost are the people who will ship the work, and it removes the handoff between strategy and delivery.
A senior engineer on the call, not a sales pitch. Thirty minutes, your actual use case, a straight answer on feasibility.
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