How On-Ground.ai works in your stores.

From setup to daily coaching, see how our AI Store Assistant fits into your teams, not the other way around.

How On-Ground.ai runs inside your stores

Fast to deploy, easy for teams to use, built to prove offline revenue impact.

On-Ground.ai listens to in-store conversations and activity, then turns that into clear signals for your teams: where you are losing intent, which stores need help, and what is working best.

  • Scope: Audio + video AI, sentiment analytics, and store CRM visibility.
  • Rollout: Start with a small set of stores, then scale across the network.
  • Timeline: Around 2 weeks from sharing basic store, team, and system details.
On-Ground.ai implementation overview

Step 1 — Scope & setup

Align on what we measure, which data we plug into, and what success means.

Scope of work

On-Ground.ai gives real-time visibility into:

  • In-store customer conversations & delivery experience
  • Sentiment and objection handling quality
  • Store advisor checklist adherence and SOP compliance

Success metrics

We agree up front on the few numbers that matter:

  • Baseline and target in-store conversion rate.
  • Average order value and attach rates for key products.
  • Shortlist of high-intent journeys to track in depth.

Implementation timeline

Estimated: 2 weeks from receipt of:

  • Promoter / advisor and manager details
  • Role-based SOP checklists
  • Lead-gen tables, product catalogue, and POS/ERP integration access
  • CCTV, device, and laptop access per store

Step 2 — What we need from your team

Team & store details

  • Sale promoters / advisors: name, store, employee ID, phone, email (if available), reporting store manager.
  • Store managers: name, store, employee ID, phone, email (if available), reporting regional head.
  • SOP checklist: operating checklist for all customer-facing staff.

Store infrastructure

  • Stable WiFi connectivity across the store.
  • Staff access to mobile devices or tablets.
  • Back-office system access for dashboards.

Integrations & access

  • Lead-gen tables from your existing systems.
  • Digital product catalogue access.
  • ERP / POS integration touchpoints.
  • CCTV feeds, advisor tablets/phones, and one laptop/PC per store for video AI.
Store and team requirements

Step 3 — Technology modules in the pilot

AI assistance for promoters on the floor and visibility for managers and leadership.

1. Store Promoter Module

  • Conversation intelligence with AI-guided talking points and objection handling.
  • Simple mobile interface for quick access during live interactions.
  • Personalised dashboards with daily conversation and performance metrics.
  • Lead creation to identify high-intent follow-up candidates.
  • Avani AI basic: chatbot to query the product catalogue.

2. Store Manager / Offline Module

  • Team performance dashboards with real-time view of advisor activity and outcomes.
  • Conversation analytics to track sentiment and effectiveness.
  • Lead tracking and follow-up effectiveness metrics.
  • Alert tracking to highlight top 3 focus areas per store.
  • AI track: audio and video repository with filters at the store level.

Step 4 — Implementation & ongoing support

Onboarding & Training

  • Technical setup and integrations with your systems (approx. 2 days).
  • In-store training for advisors and managers (1 day per store).
  • SOP documentation tailored to each user role.
  • Video modules for ongoing and new staff onboarding.
  • Refresher sessions before new campaign or season launches.

Ongoing support

  • Dedicated implementation manager for the first 30 days.
  • On-site support during the first go-live week at each location.
  • Bi-weekly progress reviews during the first 2 months.
  • Monthly optimisation workshops.
  • Quarterly business impact assessments.
  • Proactive check-ins to share best practices from other stores.

Role-based SOPs

  • Store Advisors: daily app usage and engagement protocols.
  • Store Managers: monitoring, coaching, and exception handling.
  • Leadership: interpreting insights and tracking ROI.
Implementation and ongoing support

Step 5 — Data security, SLAs & KPIs

Enterprise-grade security, clear service levels, and measurable impact.

Data security & compliance

  • Legal jurisdiction: India, with alignment to applicable laws.
  • Explicit customer consent required before any conversation analysis.
  • Role-based access control (RBAC) for all users.
  • Configurable data retention aligned to your brand’s policies.

Key performance indicators

  • Minutes of tool usage per advisor per day.
  • Number and timing of customer conversations logged.
  • Checklist completion rates and product demos captured.
  • In-store conversion rate (overall and by advisor).
  • Cross-sell / upsell attachment rate and AOV.
  • Sentiment scores and leads generated.

Service levels

  • 99.5% system availability during store operating hours.
  • Critical issues: response < 24h, resolution < 48h.
  • Major issues: response < 48h, resolution < 72h.
  • Minor issues: response < 72h, resolution within 5 business days.
  • Planned maintenance only in off-hours with 48h notice.
Data security and KPIs

Step 6 — Learning roadmap & next steps

What follows a successful pilot, and how your brand can scale.

Indicative feature roadmap (post-pilot)

  • Vernacular micro-learning library for product knowledge in multiple languages.
  • Gamified learning with badges and leaderboards to drive adoption.
  • Avani AI for store interactions and learning in major Indian languages.
  • GenAI layer to talk to store data conversationally.
  • Real-time nudges to help staff sell better on the floor.
  • Premium models to highlight priority products, stores, and staff.
  • Community features to deepen engagement across stores.
  • Lead integration with ERP / CRM / GMB / Call centre for full-funnel coverage.
  • Out-of-the-box templates for new product launches and seasonal campaigns.
  • Deeper analytics for brand / category teams with custom dashboards.

Legal terms summary (high level)

  • Initial pilot term: typically 3 months.
  • Mutual confidentiality during and after implementation.
  • Liability capped at fees paid during the previous period.
  • 30-day termination notice by either party.
  • Data return or destruction within 15 days post-termination.
  • Governing law: India; disputes via arbitration in Bangalore.
  • Standard data protection addendum (DPA) available on request.
  • Right to run anonymised benchmarks to improve platform performance.
Next steps

Next steps with On-Ground.ai

  1. Discovery session to align on objectives and confirm pilot stores.
  2. Technical assessment of current systems and integrations.
  3. Implementation planning with agreed timelines and owners.
  4. Pilot kick-off with all stakeholders and clear success metrics.

Ready to explore a pilot? Reach us at sales@on-ground.ai.

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