A forward deployed team inside your operation from week one. They watch the work, map the function that runs it, and put AI only where it earns its place. Live in one slice by week four, on your data, in your infrastructure.
Products can be described in a room and be mostly right. The work that runs a business is different: its real logic lives with the people doing the job, and you collect it by standing next to them. So that's where a Labs24 deployment starts, and why what we ship holds up on a Tuesday afternoon, not just in a demo.
Deployments, not pilots. The code runs first in your live operation, one slice at a time, and it's still running after we leave.
No two companies arrive at AI from the same place: building it, running it, rescuing it, or staffing it. Wherever you're starting, that's where the team deploys.
AI features your users actually use. Built inside your product and shipped against real usage, not a demo.
The workflow where AI should already be working. Live in one slice by week four, priced against the savings.
A pilot that never left the sandbox, or AI misbehaving live. We instrument it, run it on real data, and make it hold.
A forward deployed pod inside your team for the long build. Proven hands without the hiring cycle.
Different rulebooks, same standard. From compliance-heavy public work to high-velocity consumer operations.
Forward deployment isn't an agency with a faster invoice, and it isn't staff augmentation with a nicer name. The difference is who writes the spec, and where the code runs first.
| Question | Agency | Staff aug | Forward deployed |
|---|---|---|---|
| Who writes the spec | You do, up front | You do, continuously | We do, after watching the work |
| Where code runs first | Their staging server | Your repo | Your live operation, one narrow slice |
| Feedback loop | Milestone to milestone | Daily standup | Same day, against real data |
| Who owns the outcome | The SOW does | You do | We do, jointly and by name |
| At handover | A cliff | Nothing changes | Already running without us |
Nothing exotic. Boring infrastructure is a feature when the business depends on it.
Client names stay confidential, because every engagement runs under NDA. The builds speak for themselves.
Designed the rewards layer inside a city transit super-app: post-ride scratch cards, a coin economy, and a brand offer inventory that turns commuters into repeat riders.
→ Rewards live inside a multi-service city appBuilt a Phase 1 offer engine: merchant-funded offers, eligibility and targeting rules, and full redemption tracking, delivered as a white-label module inside their stack.
→ Offer engine shipped as a licensed moduleDesigned and built the in-app rewards section: coin earning, tiered redemption, and brand-funded coupons, modeled on engagement patterns from large consumer apps.
→ Rewards section live in the consumer appBuilt the campaign engine behind brand coupon distribution across major consumer apps: offer inventory, per-app allocation, and redemption reporting in one place.
→ Campaigns running across multiple large appsReplaced spreadsheet chaos with an internal ops tool: production and dispatch tracking, automated GST invoicing, and daily reporting pushed to the owner's phone.
→ Hours of daily manual work automatedBuilt dispatch orchestration with WhatsApp-based proof-of-delivery capture, live trip status, and an exceptions dashboard that flags delays before customers call.
→ POD capture moved from paper to same-dayBuilt a document intelligence assistant over the firm's own precedent library: clause search, first-pass contract review, and drafting support, running on self-hosted models so client files never leave their infrastructure.
→ First-pass review time cut sharplyAutomated the patient follow-up loop: appointment reminders, report delivery, and feedback capture, built data-sensitive by design with records staying inside the clinic's systems.
→ Follow-ups automated, no-shows reducedBuilt the coordination platform for brand activations across gyms: venue inventory, campaign booking, execution tracking, and settlement between brands and venues.
→ Activations for national D2C brands deliveredWired the operations stack end to end: order sync, support automation with AI-drafted replies, and review mining that turns customer feedback into a weekly product report.
→ Support and reporting run without added headcountLabs24 exists because the work that runs a business can't be specified from a conference room, and shouldn't be priced as if it could. We deploy engineers the way we build our own products: on the floor in week one, scoped from what we saw, live in one slice by week four, and running without us at handover. That standard belongs to everyone who builds here, on every deployment, without exception.
The first working demo came in week two. With our previous agency, week two was when the project plan PDF arrived. That difference is everything.
I message one person and the build moves. No account managers, no translation layer. It felt like having a technical co-founder on call.
They scoped it, priced it, and shipped it inside the quarter. Then it kept running without them. That last part is the rare bit.
A forward deployed engineer (FDE) is a software engineer who works inside a client's operation rather than from a brief: watching the work, mapping the function that runs it, and building only what that function needs, in the client's environment, on live data, into production. The term comes from Palantir, which embedded engineers on site with customers from around 2010; frontier AI labs and cloud providers now deploy the same way. Not a consultant who advises, not a contractor who fills a seat.
Day to day, an FDE sits with the people doing the job, writes down the overrides and exceptions no document captures, measures how the work runs today, then builds and ships one narrow slice on real data, runs it beside the humans it supports, and hands it over documented and instrumented. The deliverable is a system running in production, not a deck or a recommendation.
A software engineer builds one capability for many customers, from a roadmap. A solutions engineer builds demos before a deal closes; their job ends at “yes”. A consultant is paid for advice and hands you a recommendation. A forward deployed engineer builds many capabilities for one customer, writes the production code, owns delivery end to end, and stays until the system is used, trusted and producing a number you can point at.
With an agency you write the spec up front and the code runs first on their staging server. With staff augmentation you write the spec continuously and own the outcome yourself. With forward deployed engineers, we write the spec after watching the work, the code runs first in your live operation on one narrow slice, and the outcome is owned jointly and by name.
Because the model stopped being the hard part. Most enterprise AI pilots never reach production: the demo works on clean data, then breaks on real records, real systems and real people. Forward deployment closes that last mile by putting the engineer next to the work, which is why AI labs, cloud providers and enterprises have all adopted the model since 2025.
You do if there is a workflow where AI should already be working and nobody has had the hands to change it, a pilot that never reached the people doing the job, or rules that live in people's heads rather than in documents. You don't if you want a strategy deck first, or can't give a team a real shift and one decision-maker. Forward deployment is a delivery model, not a research project.
You're buying a working piece of your operation, not hours or headcount. Scoped upfront and billed against milestones you approve before we start; payment is tied to deliverables you have signed off, not to elapsed time. Most deployments run four to eight weeks: week one on your floor ends with a locked scope and a price, a working demo lands every Friday from week two, and one slice is live on real data by week four. No open-ended invoices, no surprise extensions.
Four things in week one. A real shift where the work actually happens: two or three days, not a briefing. One decision-maker who can approve a direction on a Friday. Read access to live data, under NDA, on your infrastructure if you prefer. And one slice, a team, a branch or a shift, where we're allowed to go live early and be imperfect. Week one happens where the work happens; after the scope locks, the team builds embedded in your repos and your standups, on your hours, deployed worldwide from Chennai.
NDA before we see anything. For sensitive paths we deploy self-hosted models on your infrastructure, so records never leave your systems; legal, healthcare and public-sector engagements run this way by default. You own the code and IP in full and in the contract, with code in your repositories from the first commit. At handover you get a runbook, instrumentation and a baseline, running without us.
That's the Production Rescue track. We instrument what you have, run evals to find where it breaks, then harden it with observability, guardrails and fallbacks. Fixing is often faster than rebuilding, and we tell you honestly which one you need.
Nothing here needs a contract renegotiation. It needs a calendar.
NDA, credentials, one named owner on each side.
We're on your floor. Watching the work, reading the threads, sitting in the queue.
A written plan with milestones and a number. Built from what we saw, not from a deck.
The first Friday demo lands eleven days later.