Machine learning development in Hyderabad
Pharmaceutical plants and quality teams around Hyderabad record every deviation, complaint and corrective action in writing. Over the years that becomes tens of thousands of free-text records, written by different people in different words, where the same underlying problem appears under a dozen descriptions. Recurring issues hide in plain sight because nobody can read everything.
Clustering is well suited to this. On FM360 we grouped repeat faults across tens of thousands of maintenance work orders, first as an experiment and then as a nightly production job, and the same approach finds recurring deviations, complaint themes or equipment problems in quality records. We build it into the tools your quality team already uses, show the records behind every cluster so a person can judge it, and tell you plainly when a simpler rule would do.
Hyderabad's technology economy grew around HITEC City, set up in 1998, and spread west through Gachibowli and the Financial District. It is where Microsoft runs one of its largest research and development centres outside Redmond and where Amazon opened what it described as its largest campus building, in 2019. JLL counts more than 355 global capability centres in the city, the in-house engineering and operations offices of foreign companies, employing over 200,000 people, which makes it the second-largest GCC hub in India. Those centres set the local price of an experienced engineer, and every other employer in the city, startups included, hires against them.
The second pillar is life sciences. Genome Valley, the pharmaceutical manufacturers on the city's edge and a fast-growing set of pharma GCCs make Hyderabad unusual: according to the same JLL research, nine of the world's top 18 pharma, biotech and medical device companies run capability centres here. Software in this world is about records people can trust. Quality teams need to see who changed a record, when and why. Approvals need a named person behind them. A batch, a deviation or a sample has to be traceable from start to finish. Much of that work still lives in shared drives and spreadsheets that have outgrown the job.
So the buyers here are usually one of three. GCC leaders with a mandate from headquarters and a hiring pipeline that moves slower than the roadmap. Quality, clinical and commercial teams in pharma who need a tool built properly and documented as it is built. And the city's own startups and established firms, who compete with those GCCs for every engineer and need senior people on a piece of work long before a hire can join.
We are in Bengaluru and share your time zone, so the working day is the same on both sides. Most of the work happens in your repository, your ticketing system and a shared chat channel, which is how most GCCs prefer to run anyway. You talk directly to the engineer who writes the code, and the runbooks and architecture notes are written as we go, so your permanent team inherits them.
We are the right fit for the proof of concept that wins your GCC its headcount, a funded build that needs senior engineers before hiring can staff it, or specialist work alongside your team. Start with a fixed-price two-week piece and judge us on what ships. Agencies and IT services firms can bring us in white-label under their name.
Four ways this arrives.
Someone got good results on a laptop and nobody can reproduce them. We turn it into a training pipeline with pinned data and versioned artefacts, put a serving endpoint in front of it, and record what it was evaluated on so the next version can be compared.
Hundreds of conditions routing tickets or flagging risky orders, each added after an incident. We measure what the rules get wrong on your labelled history, then test whether a classifier beats them. Sometimes the answer is fewer, better rules, and we say so.
Demand, load or staffing estimates that miss often enough to be ignored. We start with a plain seasonal baseline, backtest against the periods you actually plan around, and add complexity only where it beats that baseline on those periods.
Duplicate records, recurring faults, near-identical products under different names. Embeddings group text by meaning where keyword matching cannot, and we build the screen that lets a person correct a group rather than trust it blindly.
Repeat-fault detection that began as UMAP and HDBSCAN over 33,000 work orders and shipped as nightly incremental clustering on the same embedding model.
Read the write-up →Asked by Hyderabad teams.
How do you work with teams in Hyderabad?+
We are in Bengaluru and share your time zone, so calls, reviews and releases fit inside your normal working day. We work in your tools: Slack, GitHub and Linear, or whichever chat, repositories and ticketing your parent company has standardised on, with access through your own accounts. You talk directly to the engineer writing the code, and the decisions, runbooks and architecture notes are written down in your repository as we go.
Do you build for pharma and life sciences teams?+
Yes. We build the tools quality, clinical and commercial teams use every day: platforms where every change to a record shows who made it, when and why; approvals tied to a named person; field apps for medical representatives that work without signal; batch- and expiry-aware ordering for distributors; and document search that cites the exact source passage. The design documentation is written as we build, so your teams can see exactly what the system does.
How does your rate compare to hiring in Hyderabad?+
PayScale puts a mid-career software engineer in Hyderabad at about ₹14 lakh a year. Our published rate is $35 an hour, with a $5,000 minimum. That buys a senior engineer who writes the code, works in your Slack, GitHub and Linear, and starts within days, with no recruiting round, notice period or employment overhead. Begin with a fixed-price two-week piece at $2,800 and judge us on what ships.
Can you build the proof of concept our GCC needs to win headcount?+
Yes. We build one workflow as a small, real product inside your environment, usually in Next.js and Postgres, with usage you can report to headquarters. It starts with a fixed-price two-week piece, so the first working version arrives quickly and costs a known amount. The NDA comes before specifics, and the code sits in your repositories from the first commit, so when the headcount is approved your new team extends it.
Do we need machine learning, or is a rules engine enough?+
Often a rules engine is enough, and it is cheaper to run, explain and audit. If the people who make the decision today can write it down, write it down. A model earns its place when the rules keep multiplying, the inputs are messy text or images, or the pattern shifts faster than anyone can edit the rules. We check against your history before recommending either.
Why not just call a hosted model API?+
Sometimes you should. With low volume, messy language and no labelled data, a hosted model and a good prompt ship quickly. A trained model wins when per-call pricing starts to hurt at your volume, when latency matters, when data cannot leave your systems, or when you have labelled examples a small model can learn from. We price both paths before you commit.
How much data do we need?+
For classification, usually fewer labelled examples than people fear, and embeddings can group text with no labels at all. Forecasting is the opposite: you need enough history to cover the cycles you plan around, and a single year says little about a yearly pattern. We give you an answer after looking at your data, not before.
What happens when the model gets worse?+
It will, because the world it learned from changes. We log inputs and predictions, compare them with outcomes as they arrive, and alert when accuracy or the input mix drifts past a threshold you agreed. Retraining is a scripted pipeline with an evaluation gate, so a new model replaces the old one only when it does better on the same test set.
Can you build a recommendation system?+
Yes, and we would start without a learned model. Co-occurrence and embedding similarity, served from Postgres or a vector index, beat a most-popular list for most catalogues and are easy to debug. A learned ranker comes after that, once you have enough interaction data to train and evaluate one. A learned ranker is something we build from our work with embeddings and clustering, starting from a simple baseline you can measure it against.