How we've applied AI, machine learning, and data engineering to solve hard operational problems across legal, HR, travel, and education.
How a growing legal firm eliminated 20+ hours of manual document review per week using an AI contract analysis agent — without replacing a single lawyer.
Intelaw's legal team was spending the majority of their working hours on first-pass contract review — reading through lengthy agreements to flag non-standard clauses, identify risk areas, and extract key obligations before a senior lawyer could begin substantive analysis.
With a growing client base and increasing contract volumes, the firm faced a critical bottleneck: the most expensive resource in the firm (senior lawyers) was being consumed by work that was largely pattern recognition — exactly the kind of task AI excels at.
They had explored off-the-shelf legal software but found it too rigid for their varied contract types and couldn't justify enterprise pricing for a firm of their size.
We built a secure, document-ingestion AI agent using Claude's API, fine-tuned with legal context specific to Indian contract law and Intelaw's common contract types — NDAs, service agreements, vendor contracts, and employment documents.
The agent reads uploaded contracts, automatically extracts key clauses, flags non-standard or high-risk language, summarises obligations for both parties, and generates a structured first-pass review report in minutes. Lawyers receive a structured brief rather than a raw document.
How an HR staffing firm automated candidate screening and matching, cutting time-to-shortlist from 3 days to under 4 hours — at scale.
Siya Infotech was processing hundreds of CVs per month across multiple open roles. Recruiters spent the majority of their day reading, comparing, and manually scoring candidate profiles against job descriptions — a slow, inconsistent, and exhausting process.
Client satisfaction was suffering because time-to-shortlist was stretching to 3+ days. Recruiters were making inconsistent decisions, particularly when fatigued. And with a growing client roster, the volume was unsustainable without hiring more staff — which would compress already tight margins.
The firm needed a way to scale throughput without scaling headcount proportionally.
We built a two-layer AI system: a document processing pipeline that ingests CVs in any format (PDF, Word, plain text) and extracts structured candidate data, and an intelligent matching engine that scores candidates against job descriptions using semantic similarity and configurable weighting criteria.
Recruiters now upload a job description, and the system automatically ranks all relevant candidates in the database — surfacing the top 10 with explanation scores. Human recruiters review and approve the shortlist. The system learns from recruiter decisions over time.
How a fast-growing travel booking platform built a real-time data infrastructure to handle price feeds from 50+ suppliers — and cut load times from 12 seconds to under 2.
Visitors Deals — a MakeMyTrip-style travel booking platform — was ingesting price and availability data from over 50 airlines, hotel chains, and tour operators. Each supplier had a different API format, update frequency, and data structure.
The existing system was a patchwork of manual integrations that frequently broke, served stale prices to customers, and made search results load agonisingly slow — up to 12 seconds in peak traffic. This was directly impacting conversion rates and customer retention.
The business had no single source of truth for pricing data, no real-time analytics on booking behaviour, and no visibility into which suppliers were performing. Decisions were made on gut feel rather than data.
We designed and built a centralised data engineering platform that standardised all 50+ supplier feeds into a single normalised schema, with real-time streaming and intelligent caching to ensure prices were always fresh and search results were instant.
On top of the data layer, we built an analytics dashboard giving the commercial team real-time visibility into booking trends, supplier performance, margin analysis, and customer behaviour — replacing the weekly manual Excel reports they had been relying on.
How an edtech platform used AI-powered personalisation and a learner analytics engine to increase course completion rates from 31% to 68% in four months.
KeySkills Hub had a classic edtech problem: strong top-of-funnel enrolment but poor course completion. Only 31% of learners who enrolled were finishing their courses. The rest dropped off — often within the first two weeks — costing the platform in refunds, poor reviews, and lost referrals.
The team knew learners were struggling but had no visibility into where or why. There was no analytics layer to identify at-risk learners, no personalisation to adapt content to different learning speeds, and no automated intervention to re-engage learners who went quiet.
Customer support was also overwhelmed with basic questions about course content, schedules, and certifications — taking attention away from genuinely complex issues.
We built three interconnected systems: a learner behaviour analytics pipeline that tracked engagement signals across every course module; a machine learning model that predicted learner drop-off risk 7 days in advance; and an AI-powered nudge and re-engagement agent that automatically reached out to at-risk learners with personalised interventions.
We also built a course-content-aware AI assistant that answered 80% of common learner support questions instantly — dramatically reducing the support ticket volume and improving learner satisfaction scores.
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