AI-Powered IT Solutions That Drive Real Business Outcomes

A mid-market staffing firm was drowning in client emails. Five hundred-plus inbound requirements every day, parsed by hand into a CRM that never quite fit the shape of how requirements actually arrived. Placements were slow, recruiters were burning out, and the bottleneck was visible to every stakeholder. We replaced the manual intake-to-contract flow with a multi-agent AI system — and gave 18 hours per week back to every recruiter.

The Challenge

Every inbound JD email was different. Some had the role in the subject line, some buried it in a 2,000-word PDF attachment. Skill requirements ranged from explicit ("5+ years React") to implied ("must handle fintech compliance"). Recruiters spent an average of 22 minutes per email just normalising the data — and at 500 emails a day, that was the equivalent of seven full-time staff doing pure intake work.

The problem wasn't just speed. It was consistency: two recruiters reading the same email would extract different requirements, leading to mismatched candidate shortlists and frustrated clients.

Our Approach

We diagnosed this as a multi-agent problem, not a single-LLM-call problem. Each step in the lifecycle — classification, extraction, matching, outreach, contracting — had different accuracy requirements, different failure modes, and needed different human-in-the-loop checkpoints. Solving it with one big prompt would create a black box no one could debug.

Instead, we designed a system where five specialised agents handle the lifecycle, each with its own evals, fallbacks, and audit trail. The orchestration layer decides which agent runs when, what data flows between them, and where to pause for a human decision.

What We Built

• Intake Classifier — pre-processes inbound mail and routes new requirements, follow-ups, candidate queries, and noise into the right lanes.
• JD Extractor — a hybrid LLM-plus-regex system that pulls structured fields (role, level, skills, location, comp band, deadline) from unstructured email and PDF attachments. Trained on the firm's own past placements to learn its taxonomy.
• Candidate Matcher — vector search against the existing ATS with re-ranking based on past placement outcomes, not just keyword overlap. Returns a ranked top-10 with confidence scores.
• Outreach Drafter — generates personalised first-message drafts referencing each candidate's actual background. Recruiters approve or edit before send.
• Contract Router — once a candidate accepts, generates the contract from a template, routes to DocuSign, and pings the recruiter when signed.

Every step has a human checkpoint by default. The firm dialled checkpoints down once trust grew — but the option to step back in stays.

Results (after 90 days in production)

• 85% reduction in JD-processing time (22 min → 3 min average)
• 3.2× faster average time-to-placement
• ₹48L annualised cost savings on intake operations alone
• 0 wrong-fit placements traced to JD misinterpretation, down from 8% in the prior quarter
• 30% more requirements handled the next quarter — no headcount added

"They didn't just hand us a model — they shipped a system our ops team actually uses every day. The intake bottleneck we'd lived with for years is just… gone." — COO

Tech Stack

A regional telecom partner watched customers churn every time English-only IVR couldn't understand their queries. We replaced the IVR with a conversational Hindi voice agent built on Gemini Live — sub-4-second response times, contextual memory across the call, and a clean handoff to humans when intent gets complex.

The Challenge

The client served a primarily Hindi-speaking customer base, but the IVR system was English-only with rigid menu trees. Customers either got stuck navigating, mashed 0 to reach a human (creating massive queue pressure), or churned. Hindi-language support agents existed but couldn't scale to peak load — wait times hit 12 minutes at the worst hours.

Our Approach

We needed something that felt like talking to a human in Hindi, not a stilted IVR with Hindi labels. Three constraints drove the design: low latency (conversational pacing breaks above ~5 seconds), natural understanding for code-mixed Hindi-English (a real-world feature of Indian customer speech), and graceful escalation when the AI couldn't help.

Gemini Live API gave us real-time voice capability without chaining a separate STT → LLM → TTS pipeline — which would have added latency at every hop. We built the orchestration around it.

What We Built

• Real-time voice agent on Gemini Live, grounded in the client's domain knowledge — plans, billing terms, support flows. Handles greetings, identification, common queries, plan changes, and recharge issues.
• Intent router that recognises when a query falls outside the agent's competence (complex billing disputes, network outage reports, retention conversations) and transfers seamlessly to a human agent with a context summary on screen.
• Conversation memory within a session so customers don't have to repeat their account number three times.
• Call analytics dashboard tracking CSAT trends, escalation rates, and common failure modes — so the client's CX team can iterate on weak spots.

Deployed on Google Cloud Run with auto-scaling. Twilio handles the telephony layer.

Results (after 60 days in production)

• 70% of inbound calls fully resolved by the AI
• 3.8s average response latency (vs 5s SLA)
• 4.6/5 customer CSAT for AI-handled calls
• Average human queue time dropped from 12 min to 2 min — humans only handle the 30% that genuinely need them

"Our Hindi-speaking customers finally have support that doesn't make them press 1 for English. The voice agent is the single best AI investment we've made." — Head of CX

Tech Stack

A state government department had decades of policy documents — circulars, orders, gazette notifications — in both Hindi and English, locked behind a keyword-search interface that returned mostly noise. Finding the right precedent for a new policy decision took hours of manual reading. We built a graph-augmented RAG system that handles cross-lingual queries with formal guarantees on retrieval quality.

The Challenge

Government documents are uniquely hard for vanilla RAG. They cite each other constantly — an amendment to an order to a circular to a parent act. They use highly specific bureaucratic vocabulary. And they exist in two languages with no consistent translation. Keyword search missed obvious matches because the user queried in Hindi and the document was indexed in English. Semantic search alone hallucinated citation links between unrelated documents.

Our Approach

We treated the corpus as a graph of documents, not a flat collection. Documents are nodes; explicit citations and inferred semantic relationships are edges. Retrieval becomes a constrained walk through this graph, anchored by the user's query.

We layered two embedding spaces — a multilingual sentence encoder for cross-lingual matching, and a domain-specific encoder fine-tuned on Indian government text. The retrieval algorithm uses both, and we developed mathematical bounds on the candidate set size to keep things tractable at scale.

What We Built

• Document ingestion pipeline — parses Hindi and English PDFs (including scanned ones via Tesseract + Indic OCR), extracts citations, and builds the knowledge graph.
• Hybrid retrieval engine — combines dense vector search, graph traversal, and BM25 for terminology-heavy queries.
• Cross-lingual query layer — Hindi queries reliably find English source documents and vice versa, with no quality loss.
• Cited-source UI — every result shows why it was retrieved, with the citation path visualised.
• Continuous evaluation harness — a gold-standard test set of 500 query-document pairs from real officer use, run nightly.

Results

• 92% top-5 retrieval accuracy (vs ~40% for the prior keyword system)
• 10× faster query time end-to-end
• Average research task reduced from 3.5 hours to 22 minutes per officer
• Officers now reference cross-lingual precedent they previously couldn't find

"We're surfacing relevant precedent that we wouldn't have found in days of manual search. The cross-language piece alone has transformed how our team works." — Joint Secretary, State Government Department

Tech Stack

An auto-component manufacturer was losing two million dollars a year to unplanned equipment downtime. Existing sensor data was being collected but barely used. Cameras on the assembly line were running for safety review only. We combined both sources into a unified predictive maintenance system that flags failure modes days in advance — with a Slack-native alert flow the plant team actually pays attention to.

The Challenge

The plant had instrumented critical equipment with sensors years ago — vibration, temperature, pressure, current draw. The data was sitting in a historian database, mostly looked at only after a failure. Cameras on the line captured visual feeds reviewed by hand occasionally. Failures happened when something subtle went wrong on a press, a conveyor, or a stamping unit — usually with visible and sensor-level precursors, but no one was looking at both together.

Our Approach

The signal for a coming failure rarely lives in just one modality. Vibration anomalies plus a small change in stamping geometry plus a current-draw spike together predict a bearing failure; any one alone is noise. We built a multi-modal anomaly detection system that fuses CV with sensor telemetry, trained on the plant's own historical failure events.

The hard part wasn't the model — it was integration. We pushed inference to the edge (sub-second is the SLA), surfaced alerts where the team already worked (Slack and the plant control dashboard), and did not spam them with false positives. We tuned for high precision: missing a failure is worse than a false alarm only when alarms stay rare.

What We Built

• Vision models trained on the plant's own line footage to detect geometric anomalies, missing parts, and tool wear.
• Time-series anomaly detection on the multi-sensor feed using Isolation Forest combined with a learned autoencoder.
• Multi-modal fusion layer that weights and combines signals from both, with confidence scores per failure mode.
• Edge inference on a small GPU appliance in the plant — no data leaves the facility, latency stays low.
• Slack-native alert flow with suspected component, confidence score, recommended action, and a one-click "schedule maintenance window" button.

Results (after 6 months in production)

• 40% reduction in unplanned downtime
• 11 days average lead time on predicted failures (vs ~0 baseline)
• $2.1M annualised savings — downtime, emergency parts, lost production
• 87% alert precision — the team trusts and acts on alerts

"Maintenance went from reactive to scheduled. We're saving money but the bigger thing is morale — the team isn't chasing fires anymore." — Plant Operations Lead

Tech Stack

A B2B SaaS company was losing deals to slow follow-up. Reps were juggling 60+ active opportunities, missing the 24-hour follow-up window on hot leads, and writing generic emails when they did catch up. We built a CRM-connected copilot that monitors deal stages, drafts contextual outreach from each rep's own writing style and the company's past-win patterns, and routes drafts for one-click send.

The Challenge

Sales tools that draft generic outreach already exist — most reps ignore them because the output sounds nothing like their voice and lacks specific context from the deal. The bar was: drafts good enough that the rep sends them with minimal edits, or the tool just adds friction.

We also had to handle the political reality: reps don't trust black-box automation in their CRM. Every action had to be visible, reversible, and clearly attributable.

Our Approach

The unlock was treating each rep's own win history as a few-shot example library. When a rep is following up on a deal in Stage 3 (technical evaluation) for a fintech prospect, the copilot retrieves their past Stage-3 wins with similar prospects, uses those as in-context examples, and drafts in their voice.

We layered this on top of a stage-aware policy: different stages get different framings (educate, pressure-test, propose, close), and the copilot picks the right one from CRM context.

What We Built

• HubSpot integration with bidirectional sync — deal stage changes, email opens, call logs, and meeting notes all flow into the copilot's context.
• RAG over win-stories built from each rep's last 18 months of closed-won deals, indexed by stage, vertical, and deal size.
• Brand-voice and rep-voice fine-tuning — global tone guardrails from the company, individual style learned per rep.
• Draft review flow in a Slack-style sidebar inside HubSpot. Rep sees the draft, the context that produced it, and a one-click send, edit-then-send, or reject button.
• Audit log of every draft generated, sent, edited, or rejected — visible to the rep and to sales ops.

Results (after 90 days in production)

• 35% lift in close rate on deals where copilot drafts were sent
• 2.4× more follow-ups sent — the volume problem solved by removing the writing tax
• 78% draft acceptance rate (sent as-is or with minor edits)
• Avg time-to-first-follow-up dropped from 31 hours to 4 hours

"What surprised us was the rigour — evals, monitoring, fallbacks. It felt less like an AI vendor and more like adding a senior engineering team." — VP Engineering

Tech Stack