Challenge, architecture, solution, technology stack, and results — for every engagement referenced on the AI Solutions page.
Engagement model
Our transformation work begins with the operating reality—not with a predetermined model. We assess systems and data, modernize the necessary foundations, introduce AI where it creates practical leverage, automate validated workflows, and measure the change against an agreed baseline. The ranges shown in the visual are illustrative transformation targets, not guaranteed outcomes or substitutes for the verified case-study metrics below.
Challenge: Repetitive queries, order tracking, and product-info requests across web, WhatsApp, and internal CRMs required human intervention for nearly every interaction.
Solution: A domain-aware conversational assistant using RAG and fine-tuned LLMs, integrated directly with order APIs, ticketing, and CRM systems.
| Manual query handling | −70% |
|---|---|
| Response time | −50% |
Challenge: Manual route planning meant inconsistent delivery times, higher fuel costs, and no ability to react to environmental or traffic variation.
Solution: A reinforcement-learning route engine fusing live GPS telemetry, vehicle sensors, and environmental data (temperature, dissolved oxygen, pH), continuously learning from historical routes.
| Avg travel time | −25% |
|---|---|
| Fuel consumption | −20% |
Challenge: Market research relied on static, periodic reports fragmented across news, pricing, and competitor signals — too slow for proactive strategy.
Solution: Continuous monitoring across news, pricing, and sentiment, with an LLM insight-extraction agent, a relevancy-ranker agent, and a trend-prediction agent. A companion module automates statistical market-sizing reports (CAGR, TAM/SAM/SOM).
| Research turnaround | −70% |
|---|---|
| Response speed | 3× faster |
| Launch success rate | +22% |
Challenge: Traditional transcription failed in noisy environments, with overlapping speakers and domain-specific terminology, producing unreliable structured output.
Solution: Transformer-based noise suppression and voice separation, ASR fine-tuning with multi-pass correction, and hybrid phonetic + semantic + RAG entity recognition into structured JSON.
| Transcription accuracy | 80% → 90–92% |
|---|---|
| Manual correction time | −50% |
Challenge: Continuous multi-camera inference for belt misalignment, tears, foreign material, and human presence needed real-time alerting without saturating bandwidth or routing every frame through the cloud.
Solution: Detection models (YOLOv8, Detectron2, Mask R-CNN, PatchCore) run locally on NVIDIA Jetson AGX Orin/Xavier hardware, TensorRT-quantized (FP16/INT8); only classified events — not raw video — are sent upstream.
Challenge: Brand teams couldn't keep pace with fast-changing trends across platforms, leading to generic content and inconsistent engagement.
Solution: A trend-mining agent, brand-intelligence agent, and creative-generator agent produce brand-aligned posts and short-form video, with a performance-scoring agent predicting engagement pre-publish.
| Content production speed | 3× |
|---|---|
| Creative cost | −45 to −60% |
