Most AI consulting firms hand you a PDF and an invoice. Revex hands you a production system. We do the strategy, pick the architecture, and build the thing — so your team is running AI in weeks, not waiting on a roadmap that never ships.
You've talked to AI consultants. You've seen the decks. You might even have a “transformation roadmap” sitting in a shared drive somewhere.
None of it is running.
The reason is structural: most AI consulting firms separate strategy from implementation. One team writes the plan. Another team (or your team, or nobody) builds it. The plan gets stale. The build gets scoped wrong. Six months later you're back where you started, minus a consulting budget.
Revex exists because that model is broken. We are an AI consulting firm where the people who design the strategy are the same people who write the code. No handoff. No translation layer. No “phase two vendor selection.”
Before writing a line of code, we map your business operations, data landscape, and team workflows to identify where AI creates real leverage — not where it sounds impressive on a board slide. Deliverables include a workflow audit, AI readiness evaluation, ranked opportunity list with ROI and timeline estimates, and a written build/buy/wait recommendation. If the ROI isn't there, we tell you and you don't build. Fixed fee, no obligation to continue.
Once we've identified the highest-ROI opportunity, we design the system end-to-end: model selection (Claude, GPT-4o, open-source, or mixed), data pipeline, integration architecture with your existing stack (CRM, ERP, Slack, databases), human-in-the-loop controls, deployment plan, risk and compliance review, and a sprint plan with weekly milestones and a ship date. Not a 40-page strategy deck — a build spec our engineers start executing the following week.
We build the system we designed. Same team, same week. No RFP, no vendor search, no “phase two.” We build custom AI agents for operations, support, sales, and internal workflows; LLM-powered automation pipelines on n8n; RAG systems over your company data; AI-powered features embedded in your existing product; and dashboards where every model call is logged and every decision is replayable. Stack: Claude / GPT-4o · Supabase · n8n · Next.js · Vercel · Railway.
The hardest part of enterprise AI isn't the technology. It's getting people to use it. We scope adoption into every engagement because a system nobody uses has zero ROI. Includes a change management plan, hands-on team training with walkthroughs of the actual system, documentation and runbooks written for operators, 30/60/90-day adoption metrics and check-ins, and iteration sprints based on real usage data.
For companies running multiple AI initiatives across departments, we provide the coordination layer: a cross-department AI audit of what's been tried, what's running, what failed; unified architecture standards so new AI builds share infra, auth, and observability; a prioritized transformation roadmap; an executive reporting framework tracking AI impact in terms the C-suite reads; and a governance model for AI deployment approvals and human-in-the-loop policies.
We interview stakeholders, audit your systems and data, and map the workflows where AI has the highest leverage. You get a written assessment with ranked opportunities, estimated ROI, and a build-or-wait recommendation.
We design the system architecture, select models, plan integrations, and define human-in-the-loop controls. You approve a sprint plan with a fixed ship date.
We build the core system: agent logic, tool integrations, data pipelines, and UI. You see working software in a staging environment by end of week 5. Feedback loops are daily on Slack.
We refine based on your feedback, add observability and error handling, deploy to your infrastructure, and run the system against live data.
We stay on retainer to monitor adoption, iterate based on real usage, and expand to additional workflows. Or we hand off cleanly — repo, docs, runbooks, and model keys are yours.
Every AI consultant on our team writes code. The person who designs your strategy is the same person deploying it to production. Zero handoff friction.
You won't get a team of recent grads running prompt experiments. Our engineers have shipped production AI systems for real estate platforms, fintech apps, SaaS products, and enterprise operations tools.
Our average time from kickoff to production deployment is 6 weeks. We've built working AI agents in as little as 4 weeks.
Code, prompts, model API keys, infrastructure accounts, runbooks. From day one. No lock-in, no proprietary platform.
AI consulting engagements start at $10K for an assessment + v1 build. Enterprise transformation programs are scoped and quoted on a fixed-fee basis.
Common questions about AI consulting services, pricing, and how we work.
An AI consultant evaluates where artificial intelligence fits in your business, designs the technical architecture, and — if they're worth hiring — builds and deploys the system. At Revex, we do all three. Most firms stop at the strategy deck. We keep going until working software is deployed on your infrastructure and your team is using it.
At Revex, AI consulting engagements start at $10K for a scoped opportunity assessment and initial build. Full implementation projects typically run $15K–$60K depending on complexity, integrations, and whether you need ongoing adoption support. We quote fixed fees, not hourly rates.
AI consulting includes the strategic layer: identifying which problems to solve with AI, evaluating build-vs-buy, designing the architecture, and planning adoption. AI development is the build. At Revex, they're the same engagement because separating them is how projects stall.
Most engagements run 6–8 weeks from kickoff to a production system your team is actively using. The assessment phase is 1–2 weeks. Architecture and build is 4–6 weeks. Ongoing iteration is optional and month-to-month.
Yes. About half our clients are implementing AI for the first time. We scope the assessment to evaluate your data quality, systems, and team readiness before recommending a build. If the conditions aren't right, we'll tell you what needs to change first.
Absolutely. We pair with in-house teams regularly. Sometimes we handle the AI layer while your engineers own the product code. Sometimes we backfill capacity for a sprint or two. We hand off cleanly with full documentation and runbooks so your team can maintain and extend everything we build.
