AI Operations Platform
Build, label, align & ship AI at enterprise scale
Techfilic covers annotation and labeling, human-in-the-loop verification, RLHF, LLM/VLM-as-judge evaluation, safety guardrails, and managed workforce operations. Each capability ships as a standalone, production-ready module.
Data Annotation & Labeling
Foundational labeling workflows for text, image, audio, and structured data, covering supervised and fine-tuned model training.
Text Classification & Tagging
Multi-label and single-label classification for intent, topic, toxicity, sentiment, and custom taxonomies at scale.
Named Entity Recognition (NER)
Span-level entity labeling for people, organizations, locations, medical codes, PII, and domain-specific ontologies.
Image Bounding Boxes & Detection
2D bounding boxes, rotated boxes, and keypoint annotation for object detection, OCR regions, and retail shelf analytics.
Semantic & Instance Segmentation
Pixel-level masks and polygon annotation for autonomous driving, medical imaging, and satellite imagery.
Audio Transcription & Diarization
Speech-to-text correction, speaker diarization, timestamp alignment, and accent/dialect tagging for ASR training.
Document Parsing & Layout Labeling
Table extraction, form field mapping, reading order, and layout structure for document AI and RAG pipelines.
Conversational Data Labeling
Turn-level intent, slot filling, dialogue act tagging, and multi-turn context labeling for chatbot training.
3D, LiDAR & Sensor Annotation
Point cloud cuboids, lane lines, sensor fusion labels, and 3D mesh annotation for robotics and AV stacks.
Video & Multimodal Annotation
Temporal, frame-level, and cross-modal labeling, plus VLM-powered verification for video understanding pipelines.
Video Temporal Segmentation
Action boundaries, event detection, and timeline labeling for sports analytics, surveillance, and content moderation.
Frame-Level & Object Tracking
Per-frame bounding boxes with track IDs across sequences for multi-object tracking and behavior analysis.
Video Captioning & Description
Dense and sparse video descriptions, scene summaries, and QA pairs for video-language model training.
VLM Video Verification
Vision-language models cross-check annotated frames, detect label drift, and flag ambiguous or inconsistent segments.
Image–Text & Video–Text Pairs
Alignment labeling for contrastive learning, retrieval datasets, and instruction-tuning corpora.
Video Redaction & Privacy Masking
Face blur, license plate masking, and sensitive region annotation for GDPR/CCPA-compliant video datasets.
Human-in-the-Loop Verification
Expert review queues, consensus workflows, and escalation paths that keep AI outputs accurate before they ship.
Review & Approval Queues
Route low-confidence or high-risk model outputs to human reviewers with SLA tracking and priority tiers.
Consensus & Multi-Annotator Agreement
Majority vote, adjudication, and inter-annotator agreement (IAA) metrics to resolve label conflicts.
Gold Standard & Calibration Tasks
Hidden benchmark tasks to measure annotator accuracy, detect drift, and maintain quality over time.
Expert Escalation & Domain Review
Tiered review where general annotators handle bulk work and domain experts resolve edge cases.
Active Learning Sampling
Surface the most informative unlabeled samples for human review to maximize model improvement per label.
Model Output Correction & Editing
Human editors fix, rewrite, or reject AI-generated text, code, summaries, and translations in production loops.
Agent Trajectory Review
Step-by-step verification of tool calls, reasoning chains, and multi-step agent actions before deployment.
Alignment, RLHF & Preference Data
Industry-standard alignment pipelines covering pairwise rankings, RLHF, DPO, and constitutional AI workflows.
Pairwise Preference Ranking
Side-by-side response comparison (A vs B) to build preference datasets for reward model training.
RLHF Pipeline Orchestration
End-to-end Reinforcement Learning from Human Feedback: reward modeling, PPO/RLAIF loops, and rollout collection.
DPO, ORPO & Direct Preference Optimization
Offline alignment methods that skip explicit reward models; preference pairs drive policy updates directly.
RLAIF & AI-Assisted Feedback
Constitutional AI and LLM-generated critiques scaled with human spot-checks for faster alignment iterations.
Reward Model Training Data
Scalar and pairwise reward labels, rubric scores, and outcome-based feedback for RL fine-tuning.
Instruction Tuning & SFT Data Curation
High-quality prompt–response pairs, chain-of-thought examples, and rejection sampling for supervised fine-tuning.
Safety & Helpfulness Preference Labels
Dual-axis labeling for helpfulness vs harmlessness, used in production chat and assistant models.
LLM & VLM Evaluation / LLM-as-Judge
Automated and human-augmented evaluation harnesses: rubrics, benchmarks, and model-as-judge at scale.
LLM-as-Judge
Use frontier models to score responses on rubrics: factuality, relevance, tone, and task completion.
VLM-as-Judge for Multimodal Outputs
Vision-language judges verify image captions, chart readings, UI descriptions, and video summaries.
Benchmark & Eval Harness Management
Versioned eval suites (MMLU, HumanEval, custom domain sets) with regression tracking across model releases.
Rubric-Based Human & AI Scoring
Structured criteria with weighted dimensions (clarity, correctness, completeness) for consistent grading.
Hallucination & Groundedness Checks
Verify claims against source documents, retrieval contexts, and knowledge bases before user-facing release.
Chain-of-Thought Verification
Step-level reasoning review to catch logical errors, skipped steps, and fabricated intermediate conclusions.
A/B & Shadow Model Comparison
Side-by-side production comparisons with human and automated judges to pick winning model variants.
Safety, Guardrails & Security
Pre- and post-generation checks: content moderation, jailbreak defense, PII scrubbing, and red-team programs.
Input Guardrails & Prompt Filtering
Block or rewrite unsafe, off-topic, or injection-laden prompts before they reach the model.
Output Guardrails & Policy Enforcement
Post-generation filters for toxicity, bias, brand voice, regulatory language, and domain-specific rules.
Jailbreak Detection & Red Teaming
Adversarial prompt libraries, automated attack sweeps, and human red-team campaigns to stress-test models.
PII Detection & Data Scrubbing
Identify and mask personally identifiable information in training data, logs, and model outputs.
Content Moderation at Scale
Human + AI moderation for UGC, generated media, and community content across text, image, and video.
Bias & Fairness Auditing
Demographic parity checks, stereotype detection, and fairness benchmarks across protected attributes.
Model Security & Supply Chain Checks
Weight integrity verification, prompt leak detection, API abuse monitoring, and access control policies.
End-to-End AI Lifecycle Management
Versioned pipelines connecting dataset ingestion, labeling, training, and production monitoring.
Dataset Versioning & Lineage
Track every label change, data source, and transform with reproducible snapshots for audit and retraining.
Pipeline Orchestration
DAG-based workflows connecting ingest → label → QA → export → train → eval → deploy with retry and alerting.
Model Registry & Artifact Management
Central store for model weights, eval cards, deployment configs, and rollback history.
Continuous Improvement Loops
Production feedback → re-label → fine-tune → re-eval cycles that keep models current without full retrains.
Production Monitoring & Drift Detection
Real-time dashboards for latency, cost, quality scores, data drift, and concept drift in live traffic.
Synthetic Data Generation & Augmentation
LLM-generated training examples, paraphrasing, and hard-negative mining to expand scarce label classes.
Annotation Guidelines Management
Living playbooks with examples, edge-case decisions, and version history, synced to every labeling project.
Workforce & Operations at Scale
Managed annotation teams, certifications, throughput SLAs, and project ops for enterprise AI programs.
Annotator Onboarding & Certification
Skills assessments, domain exams, and tiered certifications before annotators access production tasks.
Workforce Scheduling & Capacity Planning
Shift management, surge scaling, and geo-distributed teams to hit deadline and volume targets.
Throughput & SLA Management
Real-time productivity dashboards, queue depth alerts, and contractual SLA tracking per project.
Payment, Incentives & Quality Bonuses
Pay-per-task, accuracy bonuses, and gamified leaderboards that align annotator incentives with quality.
Multi-Language & Locale Operations
Native-speaker pools for 100+ languages, locale-specific guidelines, and cross-lingual QA workflows.
Project Management & Client Portal
Milestone tracking, sample review sessions, change requests, and transparent progress reporting.
Compliance, SOC 2 & Audit Trails
Full action logs, data residency controls, NDAs, and enterprise security for regulated industries.
CCTV & Scene Understanding
Real-time surveillance intelligence covering video ingestion, object tracking, anomaly alerts, and scene analytics.
Multi-Camera Object Tracking
Cross-camera identity re-identification and persistent tracking across large venue footprints.
Real-Time Anomaly Detection
Detect intrusions, abandoned objects, loitering, crowd surges, and behavioral anomalies in live feeds.
Scene & Context Understanding
Semantic scene graph generation (who, what, where, and what is happening) for rich video intelligence.
Face & Crowd Analytics
Anonymized crowd density estimation, flow analysis, dwell time measurement, and attention mapping.
CCTV Dataset Annotation & Labeling
Frame-level labeling, track ID assignment, event boundary marking, and quality QA for surveillance datasets.
Edge Inference & Camera Integration
Deploy lightweight CV models directly on CCTV hardware, NVRs, and edge gateways for sub-100ms latency.
On-Device AI Deployment
Compress, convert, and deploy AI models on mobile, edge, and IoT devices, with benchmarking and runtime optimization.
Model Quantization & Compression
INT8/INT4 quantization, weight pruning, and knowledge distillation to shrink models for edge hardware.
Runtime Conversion (ONNX, TFLite, CoreML)
Export and optimize models for ONNX Runtime, TFLite, CoreML, OpenVINO, and TensorRT targets.
Edge Benchmarking & Profiling
Latency, throughput, memory, and power profiling across target devices with regression tracking.
Mobile AI SDK & Integration
Native iOS and Android SDKs wrapping on-device models, covering camera pipeline, inference, and result post-processing.
OTA Model Updates & Rollback
Push model weight updates over-the-air with version control, staged rollouts, and instant rollback.
AI Workflows & Orchestration
Pipeline orchestration using open-source models to ingest, process, evaluate, and ship AI at low cost.
AI Pipeline Builder
Visual DAG editor to chain data ingest → pre-processing → model inference → post-processing → output.
Open-Source Model Integration
Plug in Llama, Mistral, Whisper, CLIP, and other open-weight models to build cost-efficient AI pipelines.
Orchestrator & Agent Harness
Build multi-step AI agent harnesses with tool routing, retry logic, and observability built in.
AI Gateway & Load Balancing
Route requests across multiple model providers, cache responses, enforce rate limits, and log everything.
Batch & Streaming Processing
Run batch overnight jobs or real-time stream processing (Kafka, Kinesis) for time-sensitive AI workloads.
Live Stream Data & Collection
Pull real-world data from live streams, cameras, and IoT sensors, with human-in-the-loop oversight for high-stakes decisions.
Live Stream Ingestion
Connect RTSP, HLS, WebRTC, and social live streams into your AI pipeline for real-time processing.
Real-Time Human-in-the-Loop
Human operators watch live streams and make critical decisions: route alerts, approve AI flags, or intervene.
AI Data Collection Pipelines
Automated pipelines using open-source models to extract, filter, and tag data from live or archived streams.
Event-Driven Triggering
Fire downstream actions (alerts, annotations, API calls) when AI detects specific events in the live feed.
Golden Dataset Collection
Systematically curate high-quality examples from live data to build ground-truth evaluation sets.
AI Evals, Labs & Benchmarking
Build, run, and iterate on evaluation harnesses: golden datasets, model benchmarks, and structured AI labs.
Evaluation Harness Builder
Compose eval pipelines with custom metrics, LLM-as-judge, and deterministic checks, all version-controlled.
Golden Dataset Management
Curate, version, and manage ground-truth evaluation sets that anchor your model quality benchmarks.
Model Benchmarking Suite
Run standard (MMLU, HumanEval) and custom benchmarks, and compare models across releases and providers.
AI Labs & Experimentation
Sandbox environments for running experiments, testing prompts, and comparing model behaviors safely.
Regression CI for AI
Run eval suites on every model update to catch quality regressions before they reach production.
Robotics Data Collection
Capture teleoperation demos, multi-sensor recordings (RGB-D, LiDAR, IMU, joint states), and task success labels to train and evaluate robot policies.
Active tasks
Live feed
Hiring funnel
Metrics