Expertise that's been
pressure-tested in production.

Vision-Language Models are the strategic high ground of this decade — the point where the CV era and the LLM era converge into systems that see, read, and reason in a single pass. We build VLM systems for scene reasoning, visual Q&A, multimodal agents, image-text retrieval, and grounded multimodal reasoning.

Our methodology pairs foundation-model adaptation (CLIP, BLIP-2, LLaVA, Qwen-VL families) with vertical fine-tuning, contrastive alignment on domain data, and rigorous multimodal evaluation harnesses — including red-team protocols for hallucination, refusal, and modality leakage.

VLM workloads sit on our shared inference plane — vLLM, TGI, Triton — with quantization tuned to the task: bf16 where reasoning matters, int8/int4 where throughput does. Where the use case demands edge, we cascade to a smaller distilled VLM on-device.

Computer Vision isn't an adjacent capability for us — it's the discipline our founder began at UC Berkeley and the system class that powers our consumer products. Detection, segmentation, instance tracking, OCR, defect inspection, video analytics: we ship them at consumer cost and consumer latency.

We design pipelines around the deployment target first. Server-side detection runs on Ultralytics YOLO and SAM derivatives with TensorRT acceleration. On-device segmentation cascades to distilled student models compiled to CoreML, TFLite, or ONNX Runtime — quantized, pruned, and budgeted to phone-class hardware.

Production CV is as much about evaluation as architecture. We build per-class precision/recall dashboards, drift detectors, and edge-case mining pipelines so that the model in production is the same model your QA team approved.

Generative visual AI is where consumer-scale taught us the most. The cost ceiling and brand-safety floor are non-negotiable when you serve millions of users — and those constraints map exactly to enterprise creative ops.

Our generative stack pairs diffusion backbones (SDXL, FLUX-class architectures) with brand-locked LoRA adapters, ControlNet conditioning, and inpainting/relighting modules for catalog automation, virtual try-on, and brand-consistent creative. Generation is orchestrated through cost-aware schedulers that pick the right model and the right step count per request.

Generation infrastructure is hosted on vLLM and Triton with autoscaling tuned to creative ops traffic shapes — long tails of bursty requests, not steady throughput.

India ships in 22 official languages and a long tail of regional scripts; most enterprise AI doesn't. We've built proprietary OCR and NLU stacks for Devanagari, Bengali, Tamil, Telugu, Gujarati, Kannada, Malayalam, Punjabi, Odia, and Urdu — among others — at character-accuracy parity with English baselines on production data.

Our pipelines combine script-specific detection (recognizing the visual logic of conjuncts and ligatures), domain-tuned recognition models, and multilingual NLU heads built on Indic-aligned encoders. For generation, we adapt instruction-tuned multilingual models with vernacular DPO and human preference loops.

Where data is sparse, we use synthetic augmentation and cross-lingual transfer — and we share our preprocessing standards openly with customer teams so the work is portable.

We deploy LLMs where they earn their keep: domain fine-tuning, retrieval pipelines, and agentic workflows that hit real enterprise traffic — often on-prem or inside customer VPCs, with no provider lock-in.

Fine-tuning is selected by data size and behavior delta: LoRA and QLoRA for narrow capability shifts, full SFT where the gap is wider, RLHF/DPO where preference data is available. Retrieval uses hybrid sparse-dense pipelines (BM25 + dense) on Qdrant, Weaviate, FAISS, or OpenSearch, with re-ranking and chunking strategies tuned to the document corpus.

Agent runtimes are LangGraph-based or fully custom, with explicit tool use, deterministic state machines, and evaluation harnesses that test the agent, not just the model. Inference runs on vLLM, TGI, or Triton — your call.

Most enterprise value is locked inside semi-structured documents — invoices, contracts, claims, clinical reports, KYC packets. We build extraction pipelines that combine layout understanding (DETR-derived detectors), OCR, and structured prediction heads to lift fields, tables, and relationships at audit-grade accuracy.

On the language side, we run NER for domain vocabularies, summarization, topic modeling, sentiment, and conversation analytics — fine-tuned where general-purpose models miss your terms. Enterprise search and Q&A pipelines combine RAG with structured filters so answers cite real documents and stay inside permission boundaries.

Everything ships with human-in-the-loop where the work demands it: exception queues, confidence-thresholded escalation, and audit trails your compliance team can read.