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.
▸
Four-Tier Long-Term Memory — Academic,
Contextual, Personal, and Preference memory
layers. Every session opens with a personalized recap and
dynamic study plan.
▸
Dynamic Knowledge Graph — Per-student
graph that continuously updates.
Auto-advances on mastery, silently reroutes to prerequisites
on struggle.
▸
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.
▸
Custom Media Pipeline — Engineered audio
processing nodes to bypass standard heavy
wrappers, achieving sub-150ms round-trip latency.
▸
Intelligent Barge-in & Interruption —
High-fidelity voice activity VAD coupled
with prompt-cancellation mechanics for immediate
interruption.
▸
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.
▸
Multi-Model Ensemble — SAM for scene
segmentation, YOLOv8 for real-time detection,
Mask R-CNN for pixel-level instance separation. NVIDIA
DeepStream pipeline.
▸
Geospatial Intelligence — Live GPS +
haversine distance estimation. 500m geofenced
safety zones trigger simultaneous voice alerts and Firebase
push notifications.
▸
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.
▸
3,000+ SQL Tables · 150+ Warehouses —
Complex joins, nested aggregations,
multi-warehouse cross-queries at sub-3-second response
time.
▸
6+ Indian Languages — Hindi, Tamil,
Gujarati, Telugu, Marathi, Bengali with
IndicBERT and language detection pre-processing.
▸
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.
▸
Stage 1 — PyTorch → ONNX Operator-level
graph optimization, node fusion, MLIR
cross-platform operator lowering.
▸
Stage 2 — INT8/FP16 Quantization
Calibration datasets validate <2% accuracy
ceiling vs. baseline.
▸
Stage 3 — TensorRT Engine Compilation
Ampere-architecture kernel fusion, layer
optimization, memory layout tuning. OpenVINO for Intel
targets.
▸
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.
▸
Real-Time ASR — Whisper large-v3 with
<200ms latency, speaker diarization
across 99 languages.
▸
Neural TTS + Voice Cloning — XTTS-v2
synthesizes translated speech in the original
speaker's voice — preserving timbre, prosody, and emotional
cadence.
▸
200+ Language Pairs — NLLB-200 and
SeamlessM4T with contextual coherence —
preserving meaning and register, not just literal
words.
▸
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.
▸
Intelligent Requirement Analysis — Reasons
about intent, identifies ambiguities,
surfaces edge cases, converts rough briefs into specs.
▸
Automated SRS Generation — Complete
Software Requirement Specifications in 30
seconds. What took a senior engineer a full day.
▸
Smart Kanban & Auto-Assignment — Tasks
generated directly from analyzed requirements,
pre-loaded with context, priority, and assignees.
▸
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.
▸
Apache Kafka Event Streaming —
Guaranteed message ordering, consumer group
management, and exactly-once delivery semantics.
▸
CQRS & Event Sourcing — Immutable event
log for full audit trails and time-travel
debugging under concurrent transaction load.
▸
Full Observability Stack — Prometheus +
Grafana: real-time p99 latency,
transaction throughput, error rates — NPCI-class
monitoring.
▸
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
