You know you need AI.
You don’t know where to start.
Years of POS, inventory, and finance data, no path to using it. We ran 400+ retail locations before we built Ward.
Schedule a strategy session →AI vendors sell tools. Nobody helps you build the plan.
You have seen the demos. Your data is not ready, your systems do not talk, and nobody has bandwidth to start.
POS in one place, inventory in another, finance in a third. A 10-year-old ERP and a POS that cannot export clean data.
Every AI vendor claims to solve everything and none of them know your operations. What you need is someone who has stood in your stockroom.
We started on the floor: operators who became data engineers who became AI architects.
Operators who became data engineers who became AI architects.
We ran 400+ locations and built custom ERPs, POS, inventory, and data pipelines from scratch. We know what breaks at scale because we lived it.
We built the data lakes, ran the orchestration, and modernized the legacy stacks ourselves. Every recommendation has already survived a retail floor.
Strategy broken down by what you actually need.
Some need a data foundation. Others need orchestration. We meet you where you are.
An audit of data, systems, team, and workflows. Maturity scoring, integration gap analysis, capability mapping, and a prioritized roadmap.
Start assessment →POS, inventory, ERP, marketing, labor, and finance into one AI-ready layer. Data model, pipeline design, governance, cloud architecture.
Design your data lake →What to build, what to buy, how the pieces fit. LLM selection and routing, retail agent design, retrieval architecture, build vs. buy per capability.
Plan your AI stack →When legacy ERP, POS, or WMS is the bottleneck. Legacy audit, vendor evaluation, API-first architecture, phased rollout.
Modernize your stack →Pilot to production. KPI definition, monitoring and alerting, closed-loop feedback design, change management.
Deploy with confidence →First, we figure out what kind of stack you actually run.
Revenue is a lazy proxy. What matters is how many systems must talk and how much custom logic sits on top.
One POS, inventory in spreadsheets, no ERP or BI layer. 1–15 locations, one channel. Regional specialty, single-brand DTC, family grocery.
POS, ERP, WMS, e-commerce, finance, mostly off-the-shelf. 15–75 locations, custom KPIs on a standard base. NetSuite or Dynamics with a modern POS.
Retail, DTC, wholesale, marketplaces, franchise. 75–500 locations, custom domain logic, PCI scope, data engineering in place.
Multiple legal entities and ERPs inherited through M&A. 500+ locations, cross-entity benchmarking, data residency, SOC 1 and 2 in scope.
One package per stack type. Fixed scope, fixed price.
Billed at $200/hour. Every engagement ends in a finished plan you can hand to an engineer. You own everything we produce.
- Stack and data audit, three opportunities ranked by payback
- Vendor shortlist for the first AI use case
- 30-60-90 day execution plan, one executive readout
- Maturity score, target data lake, orchestration, and agent design
- Build vs. buy per capability; vendor evaluation with reference checks
- 12-month roadmap with budget envelopes, board-ready deck
- Architect, on a multi-channel domain model with custom KPIs
- Orchestration design, data governance, and access-policy framework
- RFP authoring, vendor negotiation, and reference architectures
- Fractional Head of AI / Data attached to your leadership
- Federation strategy across entities; build vs. buy on every decision
- Board prep, RFP, MSA and DPA review, coaching, compliance support
Five phases. No surprises. Everything written down.
Every package runs the same five. Diagnostic compresses them into two weeks, Platform across ten, Embedded on a quarterly loop.
Interviews across ops, IT, finance, merchandising.
Out: stakeholder map, system inventory, top-10 friction list.
Maturity, integration coverage, governance, and capability scored.
Out: scorecard, gap register, opportunity list with payback.
Architecture, build vs. buy, vendor shortlists with reference checks.
Out: target architecture, 12-month roadmap, budget, risk register.
RFP drafts, vendor negotiation, MSA and DPA review, kickoff playbooks.
Out: signed vendors, kickoff packets, metrics in writing.
Embedded only. Quarterly architecture reviews, vendor scoring, a steering-committee seat, coaching.
Compared to hiring a Head of AI internally.
The real comparison is not another consultancy. It is the headcount you were about to post.
| Hire a Head of AI / Data | Big-4 / strategy consultancy | Ward Architect package | |
|---|---|---|---|
| Base salary | $240K–$300K | n/a | n/a |
| Bonus + equity | $50K–$100K | n/a | n/a |
| Benefits + payroll tax (~30%) | $80K–$100K | n/a | n/a |
| Recruiter fee (one-time) | $60K–$90K | n/a | $0 |
| Tooling, training, conferences | $15K–$30K | n/a | Included |
| Engagement fee | n/a | $300K–$1.2M | $40K |
| Time to first deliverable | 4–7 months (recruit + ramp) | 10–16 weeks | 5 weeks |
| Operator experience | Variable. Depends on the hire. | Junior-led, partner-reviewed. | Operators who ran 400+ stores. |
| Retail-native judgment | Maybe | Rare | Yes |
| Attrition / continuity risk | High in Year 1–2 | Medium (team rotation) | None. Same team start to finish. |
| Year 1 fully loaded | $445K–$620K | $300K–$1.2M | $40K |
A senior AI lead joining an unarchitected stack spends their first six months doing the work this engagement delivers in five weeks.
We’ll write the JD, sit on the interview panel, and onboard them. Most Embedded clients graduate to internal leadership. That’s the goal.
We work alongside your lead. The fastest win for someone new in the seat is a finished architecture and a sequenced roadmap they didn’t have to build alone.
Built for operators who are ready to move.
Three kinds of retail operator get the most out of Ward’s advisory, each sitting on data they can’t yet turn into decisions.
50–500 stores. You have the data but not the team to use it. You need a partner who gets your constraints.
500+ locations. You’ve invested in systems but AI keeps stalling. You need a strategy that cuts through internal complexity.
Portfolio companies under pressure to modernize fast. Every line on your roadmap has to trace to an EBITDA number the board already has.
Tell us where you are. We’ll tell you where to start.
No pitch, no pressure. A 30-minute conversation with someone who’s been in your shoes.
You know you need AI. You just need the right starting point.
Operational experience meets implementation strategy. Let’s map it out together.
Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.