Everything you need to know before embedding a Heapx pod.
Procurement, product, and marketing leads ask us the same questions when they invite a Heapx squad into their roadmap. Use this page to understand what the first 12 weeks look like, who sits inside each pod, and how we keep delivery measurable.
Pods spin up within 72 hours, pair with your owners, and ship measurable work every week.
- Discovery + calibration workshops happen in week one, followed by documented backlog and KPI model.
- Each pod includes a strategist, design/engineering leads, data intelligence partner, and delivery manager.
- We mirror your comms stack (Slack, Teams, Linear, Jira) and share Loom recaps for async stakeholders.
01. How do Heapx pods embed with our internal team?
We run a 72-hour onboarding sprint: stakeholder interviews, tool access, and backlog digestion. From there we join your existing stand-ups, mirror your PM stack (Linear, Jira, ClickUp), and publish a shared delivery calendar so everyone can see ownership, KPIs, and risk flags.
02. How fast can we kick off once paperwork is signed?
Pods deploy every Monday. We can start discovery the same week contracts close, and ship first deliverables (audit, KPI brief, or creative concept) within 10 business days.
03. What does the first 90 days look like?
Weeks 1–3 focus on audits, research, and alignment. Weeks 4–8 move into sprint production (design systems, channel launches, CVAT pipelines). Weeks 9–12 lock in enablement assets, QA, and performance reviews before we scale scope.
04. How do you measure progress week to week?
Every pod maintains a KPI dashboard plus a burn-up chart. We track velocity (tickets closed), quality (QA notes resolved), and business impact (traffic, conversion, annotation throughput) so sponsors can see signal, not just status.
05. Can Heapx manage our CVAT or labeling workloads?
Yes. Data Intelligence pods stand up CVAT instances, define ontology, recruit/coach annotators, and plug QA into analytics so model teams can trust every frame. We can host or work inside your secured tenant.
06. Will you integrate with our BI stack and feature store?
We plug into Snowflake, BigQuery, Power BI, Tableau, Metabase, or Looker. Feature engineering happens in dbt or Python notebooks, and we document lineage/governance so your internal data team can extend our work.
07. How are pods priced?
Pods are retainer-based, scoped by velocity. A standard pod (4–5 seniors) starts at ₦6.5M / $8.5K per month, inclusive of strategy, design/dev, and data operations. Dedicated research or annotation squads are priced separately.
08. What commitment do you require?
We work in 12-week increments. That window gives us enough time to research, ship, and transition knowledge so you see meaningful outcomes before deciding to extend.
09. Do you offer post-launch or hypercare support?
Yes. Hypercare pods stay on for analytics, experimentation, or enablement. We also package playbooks, runbooks, Loom walkthroughs, and training sessions so internal teams can maintain momentum.
10. What do you need from us each week?
One empowered stakeholder for approvals, data/tool access within 48 hours, and 30 minutes for demos/retro. Everything else happens asynchronously via shared boards and recorded recaps.
Pods include strategists, designers, engineers, and data partners so you can move from audit to launch without juggling multiple vendors.