Two self-hosted LLMs running simultaneously. The visible speech-to-speech tutor
interacts live with students. The hidden text-to-text orchestrator silently injects context, memory,
difficulty signals, and guidance into the tutor's prompts in real time.
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Four-Tier Long-Term Memory — Academic, Contextual, Personal, and Preference memory
layers. Every session opens with a personalized recap and dynamic study plan.
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Dynamic Knowledge Graph — Per-student graph that continuously updates.
Auto-advances on mastery, silently reroutes to prerequisites on struggle.
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Behavioral Monitoring — Screen attention analysis, camera signals, focus
redirection for sustained engagement.
AI ModelsDual self-hosted
LLMs (Speech-to-Speech + Text-to-Text)
AgentsLangGraph · ReAct ·
Real-time prompt injection pipeline
MemoryMongoDB · Four-tier
RAG · Semantic chunking
VoiceWhisper ASR · Neural
TTS · WebRTC real-time audio
Searchpgvector / Qdrant ·
Cosine similarity memory recall
A production-grade, highly optimized Voice AI calling agent platform. Rather than using
default LiveKit/PipeCat wrappers, we engineered a custom end-to-end media pipeline delivering audio-in to
audio-out latency within milliseconds. Handles proper barge-in detection, real-time dynamic language
switching, and self-hosted LLM inference for 1000s of concurrent calls.
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Custom Media Pipeline — Engineered audio processing nodes to bypass standard heavy
wrappers, achieving sub-150ms round-trip latency.
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Intelligent Barge-in & Interruption — High-fidelity voice activity VAD coupled
with prompt-cancellation mechanics for immediate interruption.
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Dynamic Multilingual Routing — Built custom classification loops that switch
languages on-the-fly depending on user speech, without restarting the session.
AI ModelsSelf-Hosted LLMs
(Llama-3-Instruct, Whisper)
PipelineCustom Audio IO ·
Python Media Nodes · Telephony (SIP/Trunking)
Scale1000+ Concurrent
Calls · High-throughput Redis queueing
LatencyAudio-in to
audio-out within milliseconds
DeploymentDockerized
production-grade Kubernetes clusters
Multi-model computer vision ensemble processing satellite imagery and live drone
footage, detecting and geolocating threats before encounters occur — specifically designed for Kerala's
forest perimeters.
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Multi-Model Ensemble — SAM for scene segmentation, YOLOv8 for real-time detection,
Mask R-CNN for pixel-level instance separation. NVIDIA DeepStream pipeline.
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Geospatial Intelligence — Live GPS + haversine distance estimation. 500m geofenced
safety zones trigger simultaneous voice alerts and Firebase push notifications.
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Inference Latency: 1.5 seconds on high-resolution aerial imagery after TensorRT
FP16 optimization.
VisionSAM · YOLOv8 · Mask
R-CNN · Ensemble inference
VideoNVIDIA DeepStream SDK
· Multi-stream GPU
GeoGPS · Haversine ·
Polygon geofencing · GeoJSON
SpeedTensorRT FP16 · CUDA
· 1.5s inference latency
AlertsFirebase Cloud
Messaging · Voice alerts · WebSocket
Multi-stage advanced RAG pipeline with HyDE, recursive retrieval, schema-aware
chunking, graph-based navigation, hybrid search + cross-encoder reranking, and chain-of-table reasoning —
deployed across real enterprise supply chain infrastructure.
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3,000+ SQL Tables · 150+ Warehouses — Complex joins, nested aggregations,
multi-warehouse cross-queries at sub-3-second response time.
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6+ Indian Languages — Hindi, Tamil, Gujarati, Telugu, Marathi, Bengali with
IndicBERT and language detection pre-processing.
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HyDE + Chain-of-Table Reasoning — Synthetic hypothetical answers drive schema
retrieval. Multi-table join strategies planned before SQL generation.
RetrievalLlamaIndex ·
Pinecone · pgvector · BM25 hybrid
RerankingCross-encoder
(BGE) · Cohere Rerank API
LLMGPT-4o / Claude 3.5
Sonnet · Structured output
SQLQuery-plan analysis ·
Dry-run execution · Auto-correction
NLPIndicBERT · Translation
pre-processing · Lang detection
A complete, end-to-end four-stage optimization pipeline that takes Google's PaliGemma
vision-language model and makes it production-ready on Jetson Orin Nano, Raspberry Pi, Android, and iOS —
without meaningful accuracy loss.
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Stage 1 — PyTorch → ONNX Operator-level graph optimization, node fusion, MLIR
cross-platform operator lowering.
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Stage 2 — INT8/FP16 Quantization Calibration datasets validate <2% accuracy
ceiling vs. baseline.
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Stage 3 — TensorRT Engine Compilation Ampere-architecture kernel fusion, layer
optimization, memory layout tuning. OpenVINO for Intel targets.
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Results: −65% model size · <2% accuracy loss · Real-time on every target
platform.
Size−65% from baseline
after quantization + pruning
Accuracy<2% loss —
validated on calibration datasets
TargetsJetson Orin Nano ·
Raspberry Pi · Android · iOS
ToolsTensorRT · ONNX
Runtime · MLIR · OpenVINO · Core ML
QuantINT8 (CPU) · FP16
(GPU) · Mixed precision
Fine-tuned LatentSync diffusion-based lip sync model on 4,000 custom samples.
Translates, synthesizes, and synchronizes lip movements in real time across live video streams —
outperforming Wav2Lip, GAN-Wav2Lip, and Wav2LipHD baselines.
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Real-Time ASR — Whisper large-v3 with <200ms latency, speaker diarization
across 99 languages.
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Neural TTS + Voice Cloning — XTTS-v2 synthesizes translated speech in the original
speaker's voice — preserving timbre, prosody, and emotional cadence.
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200+ Language Pairs — NLLB-200 and SeamlessM4T with contextual coherence —
preserving meaning and register, not just literal words.
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Superior to all baselines — Optimized for live meeting latency on a 4,000-sample
multilingual meeting-specific domain dataset.
Lip SyncFine-tuned
LatentSync · 4,000-sample custom dataset
ASRWhisper large-v3 ·
Speaker diarization · 99 languages
TranslationNLLB-200 ·
SeamlessM4T · 200+ language pairs
VoiceXTTS-v2 ·
Speaker-conditioned TTS · Prosody
CompositingFace detection
· Landmark tracking · Real-time blend
Most software teams hemorrhage time before a single line of code is written. TaskPilot
Labs eliminates this bottleneck completely — replacing days of planning with seconds of intelligent
automation that understands intent, not just instructions.
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Intelligent Requirement Analysis — Reasons about intent, identifies ambiguities,
surfaces edge cases, converts rough briefs into specs.
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Automated SRS Generation — Complete Software Requirement Specifications in 30
seconds. What took a senior engineer a full day.
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Smart Kanban & Auto-Assignment — Tasks generated directly from analyzed requirements,
pre-loaded with context, priority, and assignees.
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AI Research Assistant (RAG-Powered) — Architecture Q&A, technology recommendations
grounded in real-time internet knowledge.
FrontendNext.js 14 · React
· TypeScript · Tailwind CSS
AI EngineGoogle Gemini 1.5
Pro · LangChain · Prompt Chaining
RAGPinecone Vector DB ·
Embedding Models · HyDE Retrieval
AgentsLangGraph · ReAct
Agents · Tool-Use Orchestration
InfraCloud-native ·
Microservices · CI/CD · JWT · OAuth 2.0
A full-fidelity simulation platform that replicates the multi-party architecture of
modern payment ecosystems — from transaction routing and event sourcing to settlement workflows and
real-time monitoring.
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Apache Kafka Event Streaming — Guaranteed message ordering, consumer group
management, and exactly-once delivery semantics.
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CQRS & Event Sourcing — Immutable event log for full audit trails and time-travel
debugging under concurrent transaction load.
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Full Observability Stack — Prometheus + Grafana: real-time p99 latency,
transaction throughput, error rates — NPCI-class monitoring.
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Kubernetes-Native — Docker Compose for dev, HPA for production-scale concurrent
transaction simulation.
RuntimeTypeScript · Bun
(3× faster than Node.js)
StreamingApache Kafka ·
Kafka Streams · Consumer Groups
PatternCQRS · Event
Sourcing · Saga Pattern
DBPostgreSQL · Prisma ORM
· Redis Distributed Locks
MonitoringPrometheus ·
Grafana · Jaeger Distributed Tracing