ML & AI studio · Bengaluru · replies in 24h

We turn noise into ML systems that ship.

Research ops, document AI, RAG MVPs, data annotation, web and mobile apps, taken from raw input to a working handoff. Fixed scope, fixed price, and one engineer accountable end to end.

Start a project See how we work

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raw input · labelling live

ScrollSix services · fixed price · you own everythingBLR --:--:-- IST

Document AI■RAG systems■Research ops■Data annotation■Flutter & Kotlin apps■Agentic workflows■Web builds■On-device ML■Document AI■RAG systems■Research ops■Data annotation■Flutter & Kotlin apps■Agentic workflows■Web builds■On-device ML■Document AI■RAG systems■Research ops■Data annotation■Flutter & Kotlin apps■Agentic workflows■Web builds■On-device ML■Document AI■RAG systems■Research ops■Data annotation■Flutter & Kotlin apps■Agentic workflows■Web builds■On-device ML■

Why spacedrift

Most AI projects don't fail on the model. They fail in the noise: loose scope, messy data, and nobody who owns the build. We run every engagement through four stations, and the person writing the code is on every call.

Lourdu Raju · Founder & ML engineer

The process

Four stations. One accountable engineer.

Station 01Day 0

Noise

We listen before we build.

You bring the problem, the data, the deadline, and the constraints. We ask practical questions and tell you honestly whether it fits a fixed-scope build.

You getA clear go or no-go after one call

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Station 02Within 24h

Parse

Scope, written down.

A fixed-price proposal: deliverables, milestones, assumptions, and exclusions. The quoted price is the boundary, so nothing drifts quietly.

You getSigned scope and a fixed price

                                                        
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        "date":       "2026-09-14",                     
        "total":      48210.00,-                        
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        "confidence": 0.982 -:                          
      }            .                                    
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Station 03Build weeks

Model

Build against the scope.

Working previews, sample outputs, and evaluation numbers at every milestone. Feedback lands while it's cheap to act on, not at the end.

You getWeekly previews with real metrics

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Station 04Handoff

Ship

Hand it over, fully.

Source code, documentation, and deployment on your own accounts, plus a handoff session. Everything we built is yours, with 30 days of support.

You getCode, docs, and 30-day support

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Services

Six things we do well.

We only take work where the output can be defined, built, tested, and handed over. No open-ended retainers and no agency layers.

Research Ops

Reproducible experiment pipelines, dataset curation, and baseline replication for PhD scholars and academic labs.

PyTorchW&BHydra

Document AI & OCR

Validated extraction pipelines for invoices, receipts, IDs, forms, and Indian-language scripts.

OCRLayoutLMIndic

RAG & AI MVPs

Knowledge-base assistants, retrieval systems, and small agentic workflows. Fixed scope, about three weeks.

RAGAgentsEvals

Data Annotation

Vision, text, and audio labels with written guidelines, two-pass QA, and agreement reported per label.

VisionNLPAudio

Web Development

Fast, accessible marketing sites, product pages, and dashboards on Next.js that you fully own.

Next.jsTypeScriptA11y

Mobile Apps

Android and iOS apps in Flutter, Kotlin, and Swift, with on-device ML and the store release handled.

FlutterKotlinSwift

24h

First reply to every inquiry

3wk

Typical AI MVP build

30d

Support after every handoff

100%

Code and IP handed to you

Contact

Tell us what you're building.

A paragraph is enough. We reply within 24 hours with questions, or with a scope, timeline, and fixed price if it's a fit.

The problem, in plain words
What data exists, and where it lives
A rough deadline
Constraints: budget range, hosting, compliance