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Engineering-as-a-Service

Senior engineering capacity, on tap.

Built for digital-natives and cloud-natives that need to ship — not staff. A dedicated pod, an embedded engineer, or a fractional staff engineer on monthly capacity. The same senior engineers who deliver migrations and architecture, available as platform-team augmentation.

Why this exists

Cloud-native and digital-native companies hit a recurring shape problem — they need senior cloud / data / platform engineering capacity, but not enough to staff a full team for it. The traditional alternatives are a Big SI bench (priced out), a staffing agency (no skin in the game), or a full-time hire (slow to onboard, hard to find, expensive to retain). The Pod is the alternative — a small, senior, accountable engineering unit billed monthly.

Pod types

  • Cloud Engineering Pod. Infrastructure, IaC, networking, IAM, observability. The team that owns your cloud foundation and keeps it shippable.
  • Data Engineering Pod. Pipelines, warehouse modelling, dbt, orchestration, BI plumbing. Ships the data layer your analytics team consumes.
  • DevOps / SRE Pod. CI/CD pipelines, deployment automation, on-call tooling, incident response, SLO instrumentation.
  • Platform Engineering Pod. Internal developer platform, golden paths, self-service infrastructure, developer-experience tooling.

Engagement models

  • Dedicated Pod. Two to four senior engineers, billed monthly, exclusive to your roadmap. Best fit when you have a roadmap that needs a small team for 2 + quarters.
  • Embedded Engineer. One senior engineer, working inside your team's standups and rituals, monthly capacity (typically 60% or 80% of full-time). Best fit when you have a team but it lacks a specific senior skillset.
  • Fractional Staff Engineer. A senior engineer at staff / principal level, 20 – 40% capacity, for technical leadership, architecture review, code review, hiring loops, and tech-debt triage. Best fit when you have a team but it lacks a senior IC.
  • Platform Team Augmentation. A small pod (1 – 3) embedded into your existing platform team to accelerate a defined scope — e.g., a migration, an IDP build, an observability rollout — for 1 – 2 quarters.

How we differ from a staffing agency

  • Senior-only. Every engineer is at least senior (7 + years); pods often include staff-level engineers. No mid-level rotation, no benched juniors.
  • Outcome-shaped. The SOW specifies what gets shipped per quarter, not just hours billed. We have skin in the game on outcomes.
  • Codified rule library. Pods bring our internal toolchain — migration packs, IaC modules, observability templates — into your stack. You get the leverage of work done elsewhere.
  • No bait-and-switch. The engineers you start with are the engineers you finish with. Backfills require your approval.
  • Knowledge handoff is in scope. Documentation, runbooks, and an onboarding plan for your eventual full-time hires are part of every Pod SOW.

Who this is built for

  • Cloud-native scale-ups (Series B – Series D) that need cloud / data engineering muscle but are 12 + months from a senior hire pipeline filling.
  • Digital-natives (SaaS, marketplaces, fintech, D2C with significant data) where engineering is a competitive moat but cloud / data engineering specifically is rate-limited.
  • Post-funding teams that have a roadmap, a target market, and budget — but the hiring market for senior cloud engineers means time-to-impact is too slow.
  • Platform teams at growth-stage companies that need to deliver a quarter's worth of work in a quarter, not three.

Who this is not for

  • Teams looking for the lowest hourly rate. We are not the cheapest option, and we are honest about that up front.
  • Teams that need a full-time CTO. We can play the fractional role and run your hiring loop, but we will not replace the role permanently.
  • Pre-product / pre-PMF startups. We build for teams that have a market; we do not consult on whether you have one.

How we engage

Most Pod engagements start with the Discovery Sprint (₹2 – 3 L / $3 – 5k, 10 days) to scope the Pod composition, the roadmap, and the success metrics. The Pod runs monthly thereafter, scope-warranted, with a quarterly review built into the SOW. Onboarding (~2 weeks) is included in the first month — code access, credentials, repo walkthrough, standup integration.

FAQ

Can we hire your engineers full-time?

We do not block direct-hire conversations; the rate and notice period are negotiated in the SOW. We would rather you tell us early than be surprised.

What if a Pod engineer doesn't fit our team?

Raise it in the first quarterly review (or sooner if it's acute). We backfill with another senior engineer within two weeks; the onboarding cost is on us.

Do you work on weekends or after-hours?

Standard schedule is your business hours plus on-call participation per SOW. After-hours work on a roadmap deliverable is a written change order, not assumed.

Who this is for

Does any of this sound familiar?

If it does, the next section explains how the engagement model is structured to address each one.

Your platform roadmap has two quarters of work. Your senior cloud engineer is leaving next month. The hiring market is 4 months minimum to fill.

An embedded engineer or pod starts in two weeks — no recruiting loop, no onboarding ramp. The SOW specifies what gets shipped per quarter, not hours billed. When your hire lands, we document everything and step away cleanly.

You've been through two staffing agencies. Both sent CVs that looked senior on paper, delivered junior work in practice, and billed regardless.

Every engineer is at least senior (7+ years). Pods often include staff-level engineers. If an engineer doesn't fit the team, raise it — we backfill within two weeks at our cost. No bait-and-switch, no bench rotation without your approval.

Your team has a data engineer and a backend engineer but nobody who understands cloud platform operations. You keep getting surprised by infrastructure incidents.

An embedded cloud or SRE engineer sits in your standups and knows your stack before the first incident. The knowledge gap is structural — the right fix is a person who stays, not a one-time engagement.

You're at Series B. The board wants an engineering review. There's no principal-level engineer internally to do it. You're not ready to hire one full-time.

A fractional staff engineer at 20–40% capacity covers architecture review, code review, hiring loops, and tech-debt triage. Billed monthly, no full-time commitment, exits when the full-time hire lands.

Your internal developer platform has been 'six weeks away from done' for eight months. The team building it is also building product features.

A platform engineering pod — 1 to 3 engineers, defined scope, fixed quarter — ships the IDP without pulling your product engineers off their roadmap. Knowledge transfer and runbooks are in scope, not an add-on.

The Migration Engine

One discipline, every engagement.

Every migration runs the same six-stage pipeline: Inventory, Plan, Convert, Validate, Reconcile, Report. Human sign-off gates enforce the stage boundaries that matter.

STAGE 1 INVENTORY Read-only sweep STAGE 2 PLAN Wave + dependency map HUMAN GATE STAGE 3 CONVERT Rule library + handlers STAGE 4 VALIDATE Checksums + diffs STAGE 5 RECONCILE Daily diffs, parallel run STAGE 6 REPORT Co-signed cutover doc Scope sign-off required before conversion begins
Read how the engine works stage by stage
Why us

The rest of the market vs. what we do differently.

Every promise in this table lives in the contract, not the pitch deck.

Dimension Traditional T&M SI Replatform
Pricing model Time & materials — final cost unknown at project start Fixed-fee against a written Appendix A — no surprises
Staffing Senior pitched, junior delivered — bench economics drive the swap The engineer on the first call is the engineer in the repo
Validation Verbal sign-off or sampling — "it looks right" Deterministic row counts, checksums, query diffs — signed artefact you keep
Scope creep Absorbed into T&M — change orders often verbal, billed later Anything out-of-scope is a written Change Order before work begins
Timeline Months of ramp, discovery, re-discovery, re-scoping Fixed Discovery Sprint produces inventory + wave plan before you commit to execution
How to engage

How to start an engineering engagement.

A quick cost read, a scoping sprint to define the pod and roadmap, then monthly capacity. Each step is a standalone deliverable — stop at any point and take the output elsewhere.

01 · Free

FinOps Quick-Check

Free
2 minutes · no email

If cost efficiency is part of what the pod needs to deliver, start here to understand the current waste profile before scoping the engagement.

Run the Quick-Check
02 · Cost review

Cloud Cost X-Ray

Free
through Q3 2026 · 90-min live session

Useful before a Data Engineering Pod engagement to understand the current warehouse spend and whether cost engineering should be part of the pod's initial scope. After Q3 2026 reverts to $100.

Send me your bill
03 · Scoping sprint

Discovery Sprint

Fixed-fee
₹2–3 L / $3–5k · 10 business days

Most Pod engagements start here: maps your current stack, scopes the pod composition and roadmap, produces success metrics and the monthly capacity quote. Produces a written deliverable regardless of next step.

Start with a Discovery Sprint
04 · Ongoing

Engineering Pod or Embedded Engineer

Contact
scope-quoted · monthly capacity

Cloud Pod, Data Pod, DevOps/SRE Pod, Platform Engineering Pod, or a single embedded or fractional engineer. Monthly capacity, quarterly auto-renew, senior-only. Knowledge handoff in scope.

Talk to us
Founder-led delivery

The people doing the work.

Yash Maheshwari
Founder · Replatform

Cloud and data platform engineer with 6+ years across migrations, lakehouse architecture, FinOps, and managed platform operations on AWS, GCP, Azure, Snowflake, Databricks, BigQuery, and Redshift. The engineer on your first call is the engineer in your repo — no bench hand-offs, no junior substitutions.

Not ready to talk?

Download the Migration Readiness Checklist

If a migration or platform build is the first project for your pod, this checklist covers what to have ready — stack inventory, access posture, compliance constraints, and the engineering questions to answer before work starts.

Get the checklist