The variable workforce, enterprise grade

Your cloud scales on demand. Your workforce should too.

Enterprises are moving capital from fixed headcount into AI. The work hasn't gone away — it's changed shape. Sharedpro gives you pre-vetted engineering capacity — delivery squads to AI-native roles — deployed in 24 hours, scaled with your roadmap, and released without severance risk.

800+ vendor partners · 70+ cities · 96% joining ratio · Backed by 100X.VC & IIM Ahmedabad

Capacity, two waysInteractive

Drag your quarterly demand. Watch what fixed headcount does to your P&L.

Owned headcount (fixed at 30)

Perfectly sized — todayDemand never stays put. Next quarter this column is wrong in one direction or the other.

Sharedpro elastic capacity

Ready either wayScale up in 24 hours or release capacity same quarter. Elasticity is the point.
Annualised exposure you avoidZero — until demand moves

Illustrative model · assumes ₹2.4L/month fully-loaded cost per engineer

Trusted by Global Leaders

What's actually happening

The budget didn't disappear. It moved.

AI is pulling enterprise capital from operating expense into capital expense — from permanent payroll into compute, tooling, and automation. But every rupee of AI CapEx still needs engineers to deploy, integrate, and run it. What enterprises no longer want is to own that labour permanently. They want capability on demand — the same way they buy compute.

95%of enterprise AI pilots deliver zero measurable P&L impact (MIT, 2025). The bottleneck isn't models — it's engineers who can make them work in production.
$9B+committed in 2026 alone by Microsoft, AWS, OpenAI & Anthropic to embed engineers inside enterprises. Capability on demand is now the industry's operating model.
Fixed VariableThe structural shift: talent as elastic capability, not permanent liability.
When companies call us

Four situations. One phone call.

If one of these looks like your quarter, the capacity already exists in our network — it just hasn't been pointed at your roadmap yet.

AI programme

The AI integration squad

Board approved the AI budget; nobody approved 18 months of hiring. Get a build squad — LLM app engineers, data, MLOps — assembled from live supply and shipping inside the same quarter.

Squad assembled from live supply · not job ads

6–10 engineers3–6 monthsScale down post go-live
GCC expansion

The Tier-2 ramp

Your new centre needs 30 seats filled on a schedule the local market can’t meet. Phase the ramp through our vendor network — supply that’s already in 70+ cities.

Phased seats per month · ramp meets plan

20–50 seatsPhased quartersConvert the keepers
Critical gap

The release rescue

A resignation two sprints before release, or one missing specialist blocking the whole team. One verified profile, AI assessment report attached, working within days.

Sprint timeline · gap filled in 24h · release on date

1–3 specialists24-hr matchShort engagements ok
Vendor transition

The clean handover

An underperforming staffing vendor on the way out, a roadmap that can’t pause. Overlap new deployed talent with the outgoing team so delivery never blinks.

Three-week overlap · zero delivery days lost

Like-for-like seatsZero-gap transitionSingle MSA
Built for the people who own the decision

Same platform. Three different wins.

Workforce decisions now run through finance, engineering, and site leadership together. Pick your seat — the story rewrites itself.

The problem · headcount is a 3-year liability in a 6-month planning world

Convert fixed cost into variable capability.

Pay for deployed months only scale down at project end with zero severance exposure and no restructuring headlines.

100%variable

Workforce cost that tracks the roadmap up in build quarters, down after go-live. Auditable, predictable, reversible.

The mechanism

Supply that exists before you ask for it

Traditional staffing starts sourcing when you sign. We invert it: our partner network holds pre-identified, pre-vetted engineers — so matching starts from availability, not from a job ad.

Define the capability

Tell us the stack, seniority, and duration — a squad for an AI integration, or a single specialist to unblock a release.

30-min scoping call

Match from live supply

We match against engineers who are already assessed and already available inside our 800+ partner network.

Within 24 hours

Deploy and deliver

Talent starts on your project under your direction, with contracts, compliance, and payroll handled on our rails.

Start within 24–48 hrs

Scale up or release

Extend the squad when the roadmap grows, or release capacity the month a project ends — no severance, no bench, no restructuring headlines.

Your call, per quarter
35,000+ engineers on partner benchesEPFO verified employment historiesSingle MSA · PF & ESIC on railsTier-2 GCC hubs covered
Magic Match · instant supply preview

See your shortlist before the first call.

Give it one work email. The agent reads your careers page, parses the open roles it finds, and matches them against 35,000 verified bench engineers — while you’re still on the page.

  1. You share one work email.No forms, no scoping call, no account.
  2. It reads your careers page like a researcher.Finds your open roles and parses the JDs.
  3. 35,000 bench engineers, filtered live.Matched on stack, seniority and location.
  4. Verified profiles, AI interview reports attached.Evidence in hand before you book a call.
Try Magic Match on your roles

Opens the live product · works with your ATS, Slack and Teams

Verified bench
35,000engineers available now
Reading yourcompany.com/careers
6 open rolesJDs parsed
14 engineers match
Senior Java · Pune92
LLM apps · Bengaluru89
MLOps · Hyderabad87

AI interview report attached

Built in-house · the engine room

The AI we built to make 24 hours possible.

"AI-driven" usually means someone bought a tool. We build the agents, models, and rails ourselves — one for each stage your requirement passes through. Scroll — the engine on the right follows the story.

Stage one · supply activation agent

Your requirement reaches 800+ vendors in minutes.

A WhatsApp-native agent broadcasts the spec across our partner network, collects live availability, and aligns rates — so matching starts from engineers who exist and are free, not from a job ad and hope.

See how deployment works →
Stage two · CV intelligence

Hundreds of CVs in. A shortlist worth your time out.

NLP models read every incoming profile — skills, real experience, fitment signals, the details buried below the keywords — and collapse the pile before a human hour is spent on it.

What reaches your shortlist →
Stage three · AI interviewer

Structured interviews at any volume, scored on evidence.

Our own interviewer runs the technical and behavioural rounds — consistent, bias-checked, never tired at candidate #200 — and produces an evidence-backed report for every profile you receive.

Explore the AI Interviewer →
Stage four · delivery-physics scoring

Communication, scored like physics — not vibes.

Pace, fillers, mid-clause halts, self-repairs, pitch variety: every number countable from the clock and the waveform, every claim backed by a playable clip from the answer itself.

See it measured, live →
Stage five · verification rails

History verified against records, not the CV.

Employment history checked against EPFO records, credentials confirmed at source, and PF/ESIC statutory documentation handled on the same rails after onboarding.

Every stage above emits evidence — it all lands in the profile you receive.

The engine room · Supply activation
JD · React squad · Pune
Reading the JDBroadcastingWhatsAppEmailPuneBengaluruIndore70+ cities · WA + Email
800+ vendors see your requirement the same morning — first responses in 11 min.
214 profiles inReading below the keywords
Senior React6 yrs · fintech · joins in 15d
React + Nodeled a 3-dev pod · product co.
QA automationCypress · 5 yrs · same domain
214 CVs read below the keywords → 12 worth your interview time.
Live interview · full-stack JavaReading CV · JD live
Assisted-answer checkPassed · 3 improvised probesBehavioral evidence
AI InterviewerYou mentioned caching user sessions in Redis. Walk me through that choice.
Candidate"We moved session state to Redis so login would work across instances after we scaled…"
Follow-up generated from her answer · 0.4s
AI InterviewerWhat happens to your approach when that cache is cold, right after a deploy?
Candidate"Good question — we'd fall back to the database and rebuild entries lazily…"
Audio inAnalysis out ↓
Transcript · word-level timing"So the, um, the way I'd— I'd approach caching here ▌ 0.9s is to start from the read pattern…"FillerSelf-repairMid-clause halt
Counted from the clock, not opinion: 2.1/min fillers · 0.8/min halts — every number → a playable clip.
EPFO employment history3 roles · tenure & designation checked at source3 of 3 match
Credentials at sourcedegrees & certifications confirmed with issuersconfirmed
PF · ESIC documentationstatutory docs generated on the same railson rails
History, credentials, compliance — verified before a profile ever reaches you.

And the model we built — then fenced off.

We also built a 48-emotion Expression Measurement model, precise enough to ship as its own API. It powers learning and training only — students rehearsing for placements, employees sharpening how they present — a private mirror, with consent. It never scores a candidate. That rule isn't a policy page; it's enforced in our build.

# runs on every releasetest_no_emotion_vocabulary_in_hiring_reports✓ PASSED — build fails otherwise
Reference standard: EU AI Act Art. 5(1)(f) — held everywhere we operateExplore the Expression API →
New · for teams starting their AI journey

Not sure where AI fits your process yet? Start with a sprint.

A fixed-scope, two-week AI Capability Sprint with the same team that built the engine above. We map one process, prototype the transformation on your data, and hand you a build plan priced as a squad — deployable from the network the moment you say go. The forward-deployed model, at India economics.

Fixed price · model-neutral · your data and IP stay yours

Week 1
Map the processWorking sessions with your leads to pick the one workflow where AI moves a number you care about.
Week 2
Prototype on your dataA working demo against your real workflow — not a slide deck about one.
Output
Build plan + squad specArchitecture, cost model, and a deployment-ready squad spec — live from the network in 24 hours when you're ready.
Quality, verified before you meet them

Only 1 in 10 reaches your shortlist.

Speed means nothing if the profiles are noise. Every engineer in our network clears six verification layers before they're eligible for deployment — and every profile you receive comes with the evidence attached.

100%
Assessed
10%
Reach final stage
24h
To deployment
RS
Backend Engineer
Java · Spring · AWS · 6 yrs
Top 10%
87/100Deployment ready
AI interview · structured 40-min
Problem solvingStrong
System designStrong
CommunicationClear · structured
Live coding · candidate’s own stack
Test suites3 / 3 passed
Percentile92nd
Soft skills · assessed in context
Collaboration
Ownership
Communication
Team-fit verdictDeploy with confidence
GitHub · contribution history
Active history3+ years
Portfolio vs claimsMatches
Credentials · confirmed at source
B.Tech, Computer ScienceConfirmed
AWS Solutions ArchitectValid · current
Discrepancies foundNone
Employment history · employer chain
Roles verified3 of 3
Tenure & designationMatch
Exit termsClean
📄 Full CV◆ AI assessment report

See exactly what lands in your inbox with every profile —Get a sample verified profile pack →

The alternative you're probably considering

Freelancers work for themselves. Our engineers answer to an employer.

Gig marketplaces are fine for a landing page or a logo. When the work is core — a product roadmap, an AI integration, anything touching your IP — you need capacity someone stands behind.

Accountability

Sharedpro advantage
✕  Freelance marketplace

An individual, accountable to their next gig.

✓  Sharedpro

An employed engineer, backed by a vendor partner and a Sharedpro engagement SLA. Someone answers when something slips.

Risk · freelancers
Risk · Sharedpro

Control & direction

Sharedpro advantage
✕  Freelance marketplace

Their hours, their tools, async by default.

✓  Sharedpro

Works under your direction — your standups, your sprints, your tooling, full-time on your project.

Risk · freelancers
Risk · Sharedpro

Continuity

Sharedpro advantage
✕  Freelance marketplace

Mid-project disappearance is your risk to absorb.

✓  Sharedpro

Continuity backed by the employer chain — with replacement from live supply if it’s ever needed.

Risk · freelancers
Risk · Sharedpro

Vetting depth

Sharedpro advantage
✕  Freelance marketplace

Self-reported profiles, star ratings, portfolio links.

✓  Sharedpro

Six verification layers, top 10% pass, AI assessment report delivered with every CV.

Risk · freelancers
Risk · Sharedpro

IP & exclusivity

Sharedpro advantage
✕  Freelance marketplace

Parallel commitments are invisible; NDAs are hard to enforce against an individual abroad.

✓  Sharedpro

Full-time, exclusive deployment written into the engagement — NDA and IP terms enforceable through the employer chain.

Risk · freelancers
Risk · Sharedpro

Onsite presence

Sharedpro advantage
✕  Freelance marketplace

Remote-only, almost always.

✓  Sharedpro

Open to onsite wherever feasible — 70+ cities of supply make hybrid and on-premise real options.

Risk · freelancers
Risk · Sharedpro

Support & compliance

Sharedpro advantage
✕  Freelance marketplace

Payments, contracts, and disputes are on you.

✓  Sharedpro

Contracts, payroll, and statutory compliance on our rails, with a dedicated engagement team through the project.

Risk · freelancers
Risk · Sharedpro

To be fair to freelancers: for small, self-contained tasks they're often the right call. This table is about sustained, core engineering work — where the cost of the wrong model shows up three months in.

Convinced the math beats marketplaces?Price the alternative →

Representative engagements

What deployment looks like in the wild.

Three engagement patterns from our network, anonymised. Request the full references →

Fintech GCC · Ahmedabad

A 24-seat AI platform squad, at full strength in six weeks

A global fintech's new Tier-2 centre needed a data-platform team on a schedule local hiring couldn't touch. We phased deployment through three vendor partners — first engineers working in week one, full squad by week six.

24seats
6 wksto full squad
100%joined

"The ramp plan stopped being the risk on our board slide."

Enterprise SaaS · Series C

A release rescued after a two-sprint-out resignation

A lead backend engineer resigned two sprints before a contractual release. We matched two verified specialists overnight from live supply — AI assessment reports reviewed same day, both onboarded within 48 hours.

48 hrsto deployed
2specialists
On daterelease shipped

"We interviewed Monday evening. They were committing code Wednesday."

IT services major · Vendor exit

A 15-seat vendor transition with zero roadmap pause

An underperforming staffing vendor was being exited mid-programme. We overlapped deployed engineers with the outgoing team across a three-week handover — knowledge transferred, delivery cadence never dropped.

15seats swapped
3 wksoverlap
0days lost

"The board never knew there was a transition. That was the point."

Configure it yourself

Scope your squad. Price the quarter. Skip the hiring cycle.

Set the shape of the capability you need — including AI engineers across LLM apps, agents & RAG, and MLOps. The model prices it live, shows when the squad can be on your project — and what the traditional route burns while you wait.

Roles you need
Squad size6 engineers
Seniority mix
Engagement length6 months
Deployment order Draft
BackendData EngAI / LLM apps
First verified profiles on your desk24 hrs
Full squad live on your projectWithin 1 week
Indicative monthly run-rate₹15 L / mo
Engagement total (fully variable)₹90 L
Severance exposure at wind-down₹0
Traditional route for the same squad: a 52-day hiring cycle and ~₹26L in vacancy cost before a single line of code ships.

Illustrative pricing · final rates depend on stack, location, and onsite mix

De-risked by design

Your first 30 days, mapped.

The fastest way to trust a model is to see exactly what happens after you say yes — and where every exit door is.

Day 0

Scoping call

30 minutes: stack, seniority, duration, budget. We map it against live supply while we're still on the call.

Day 1

Verified shortlist

Profiles from the top 10% — each with full CV and AI assessment report. You interview only the ones you like.

Day 2–5

Deployment

Selected engineers start on your project, under your direction. Contracts and compliance already on our rails.

Day 30

Checkpoint

Structured review: continue, scale up, swap, or release. The decision is yours, every month after too.

Replacement from live supplyIf a fit isn't working, we re-match from the network — your project doesn't wait on a new search.
Pay for deployed months onlyTransparent monthly pricing. No bench cost, no severance exposure, no surprise line items.
Scale decisions every quarterCapacity flexes with your roadmap — up for build phases, down after go-live. No long lock-ins.
Convert the keepersStandout performers can join your payroll on transparent, pre-agreed conversion terms.
Why we saw this coming

Built for the last workforce shock. Ready for this one.

MAY 2020

The pandemic broke fixed employment

We founded Sharedpro on a simple thesis: if talent could move between organizations instead of being laid off, jobs would be safer for employees and payrolls safer for companies. We built the rails for portable employment when the world needed them most.

2026

AI is making the shift permanent

This time the driver isn't a demand shock — it's capital reallocation. Enterprises are restructuring around AI and refusing fixed labour commitments. The same rails we built in 2020 are now the infrastructure the whole market needs.

The mechanism never changed. The market just caught up to it.

Before you ask

The questions every buyer asks first.

Can I hire AI engineers on contract in India?
Yes — AI engineers are a first-class supply category on Sharedpro: LLM application engineers, agents & RAG specialists, and MLOps, deployable within 24 hours from our vendor network across 70+ Indian cities, on monthly contract terms under a single MSA.
Is this staff augmentation or project outsourcing?
Staff augmentation, under your direction: engineers work in your tools, your standups, your codebase. Sharedpro handles the sourcing, vetting, contracts, and PF/ESIC statutory compliance of IT staffing under one MSA — you keep full technical control. We are not a project-outsourcing shop.
How fast can engineers actually start?
First verified profiles reach you within 24 hours of the scoping call. For 1–3 seats, engineers typically start within 24–48 hours of your selection; larger squads deploy in phases over one to three weeks. The speed comes from supply that already exists — we match against pre-vetted, available engineers rather than opening a search.
Who actually employs the engineers?
Engineers are employed by our vendor partners — established firms in our 800+ partner network. They work full-time under your direction (your sprints, your tooling, your standards) while contracts, payroll, and statutory compliance run on Sharedpro rails under a single MSA with you.
How does pricing work?
A transparent monthly rate per engineer, set by stack, seniority, and location. You pay only for deployed months — no bench cost, no severance exposure, no hidden line items. Scale decisions happen at monthly checkpoints, so cost tracks your roadmap in both directions.
What about IP protection, NDAs, and security?
Deployed engineers sign your NDA and security requirements, and the obligations are enforceable through the employer chain — unlike agreements with individual gig workers. Engineers are deployed full-time and exclusively to your project, with exclusivity written into the engagement terms — not left to good faith.
What happens if a deployment isn't working out?
You raise it at any point — not just the Day-30 checkpoint — and we re-match from live supply so your project doesn't pause on a new search. Structured monthly reviews give both sides a standing forum to continue, scale, swap, or release.
Can we hire someone permanently?
Yes. Standout performers can convert to your payroll on transparent, pre-agreed terms written into the engagement from day one — no surprise buyout negotiations. Many clients treat deployment as the world's most rigorous probation period.
Do engineers work onsite?
Wherever feasible, yes — onsite and hybrid deployments are real options, not exceptions. With supply across 70+ Indian cities, we can often match engineers in or near your delivery location, including Tier-2 GCC hubs.

Model your next quarter with elastic capacity.

A 30-minute working session: we map your roadmap against variable capacity, show you the live supply that matches it, and give you a cost model your CFO will actually like.

No commitment · deployment possible within 24 hours of signing

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Backed by 100X.VC & IIM Ahmedabad · Founded May 2020
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