AI That Solves a Defined Business Problem — Not a Demo That Impresses No One
We build AI systems with defined failure modes, measurable accuracy thresholds, and the operational infrastructure to keep them honest in production.
GPT-4o
LLM integrations in production
RAG + fine-tuning
Both approaches, pick the right one
MLOps
Monitoring and retraining pipelines included
You've seen enough AI demos. You need something that runs in production, handles real data, degrades gracefully when it's uncertain, and doesn't require a PhD to maintain.
The graveyard of AI projects is full of impressive demos that never made it to production — models trained on the wrong data, LLM integrations with no guardrails, automation workflows that broke the moment the input data changed. We've inherited enough of those to know what they look like from the start.
We work on problems where AI provides a measurable, defensible improvement over the current manual or rule-based process. That means we spend the first phase of every engagement pressure-testing whether ML is actually the right tool — and sometimes recommending a deterministic algorithm instead. That honesty is what makes the projects we do take on succeed.
Product teams integrating LLM capabilities (chat, search, summarisation, extraction) into existing products
Operations teams with high-volume repetitive decisions that could be automated with a classification model
Data teams who have labelled datasets and need a production ML system, not a Jupyter notebook
Startups building AI-native products who need an ML engineering team, not a consultant with a slide deck
Enterprises evaluating AI for a specific use case and needing a proof-of-concept that's actually production-shaped
How we keep AI systems honest
LLM Integration & RAG Pipelines
GPT-4o, Claude, or Gemini integrated into your product with retrieval-augmented generation, document chunking, embedding pipelines, and guardrails that prevent hallucination from becoming a support ticket.
Predictive Models with Defined Thresholds
Classification, regression, and forecasting models with agreed accuracy, precision, and recall thresholds documented before training begins. You know what 'good enough' looks like before you spend the budget.
Computer Vision Systems
Object detection, image classification, and visual inspection systems built for real industrial and commercial conditions — not controlled benchmark datasets.
NLP & Text Processing
Named entity recognition, document classification, sentiment analysis, and information extraction pipelines — both fine-tuned transformer models and prompt-engineered LLM workflows.
MLOps & Retraining Pipelines
Model versioning, performance monitoring, data drift detection, and automated retraining triggers. Production models degrade — we build the infrastructure to catch it before your users do.
Human-in-the-Loop Design
For decisions that carry real risk, we design workflows where the model flags uncertainty and routes to a human reviewer. AI augmenting judgment, not replacing it without oversight.
The stack — and why
We build with the OpenAI and Anthropic APIs for LLM work, and PyTorch or scikit-learn for custom model training depending on the problem. We're not dogmatic — the tool matches the problem, not our portfolio page.
Discuss your tech requirementsBackend
LLM
Orchestration
Vector DB
ML Frameworks
Models
Classical ML
MLOps
Pipelines
Cloud ML
From hypothesis to production model
Problem Framing
Week 1We define the decision the AI needs to make, the input data available, the acceptable error rate, and what happens when the model is uncertain. If the problem isn't well-framed, nothing else matters.
Data Assessment
Week 1–2We audit your existing data — volume, quality, label coverage, class imbalance. We tell you whether you have enough to train on, or whether you need a different approach (prompt engineering, few-shot learning, retrieval).
Proof of Concept
Week 3–6A production-shaped (not notebook-shaped) prototype: real data, real infrastructure, and a benchmark against your current manual process. You can test it. You can break it.
Production Build & MLOps
Week 6–12Model serving API, monitoring dashboard, retraining pipeline, and integration into your existing system. Deployed with alerting for accuracy degradation and data drift.
Why most AI projects fail — and how we don't
We'll tell you when AI is the wrong answer
If a rule-based system or a simple statistical method solves the problem more reliably and cheaply than an ML model, we'll say so. Recommending unnecessary complexity is how consultants generate follow-on work — we'd rather you trust us for the next project.
Accuracy thresholds agreed before build
We define what 'working' means numerically before we start training — precision, recall, BLEU score, latency budget. You're not evaluating a subjective demo. You're verifying against agreed criteria.
Production-shaped from prototype
Our proof-of-concept is built on the same infrastructure as the final system — not a Jupyter notebook that needs to be completely rewritten before deployment. The transition from PoC to production is a handoff, not a rebuild.
MLOps is included, not sold separately
Model monitoring, drift detection, and retraining triggers are part of every production AI system we deliver. A model without operational infrastructure is a liability — it will degrade silently and you won't know until the damage is done.
Sectors we've delivered for
Have a specific AI problem — not a general curiosity?
Tell us what decision you're trying to automate, what data you have, and what the current manual process costs. We'll tell you in writing whether it's a good candidate for ML.
Questions we get before every ai & machine learning project
Do we need a large dataset to use AI?
What's the difference between integrating an LLM and training a custom model?
How do you prevent AI hallucinations from reaching end users?
What happens when the model starts making worse predictions over time?
Can you work with our existing data pipeline or data warehouse?
What does a typical AI project cost?
We already have a data science team. Can you work alongside them?
Is our data safe when you're building AI models?
What is RAG and when should we use it instead of fine-tuning?
How much does an AI integration or machine learning project cost in India?
Related services
Cities we serve
About our ai & machine learning services
Spacelinkers is an IT services company based in Noida, India, providing ai & machine learning to clients across India, the United Kingdom, the United States, Canada, Australia, and the UAE. Our ai & machine learning practice covers llm integration & rag pipelines, predictive models with defined thresholds, computer vision systems, and is delivered by engineers who have production experience with the relevant technology stack.
We work on problems where AI provides a measurable, defensible improvement over the current manual or rule-based process. That means we spend the first phase of every engagement pressure-testing whether ML is actually the right tool — and sometimes recommending a deterministic algorithm instead. That honesty is what makes the projects we do take on succeed.
For businesses in the Delhi NCR region, we offer in-person discovery sessions at our Noida office. For clients in Bangalore, Mumbai, Hyderabad, and other Indian cities — and for international clients — all engagements run remotely with structured communication and weekly video syncs. Contact us to discuss your ai & machine learning project, or explore the areas we serve.
Comparison guides
- Offshore development company vs freelancersWhen each engagement model makes sense, and what to look for in either case.
Building a startup? Our end-to-end startup execution service combines ai & machine learning with legal setup, branding, and growth — all under one roof.
Have a specific AI problem — not a general curiosity?
Tell us what decision you're trying to automate, what data you have, and what the current manual process costs. We'll tell you in writing whether it's a good candidate for ML.
Or email sales@spacelinkers.com — written quote within 48 hours.