SERVICES · ENTERPRISE AI
Enterprise AI Services.
Built by a team that ships to 50 million users. Productized for your enterprise stack, your data, your compliance posture.
PACKAGES
Six fixed-fee packages.
Choose where you are.
From early scoping to running production AI — every phase has a named package with timeline, team, and outputs declared up front.
PACKAGE
Discovery Sprint
For leadership teams scoping AI investment.
TIMELINE
2–3 wks
Fixed fee
TEAM
- AI strategist
- ML architect
OUTPUTS
- · AI maturity audit
- · Prioritized use-case backlog (value × feasibility)
- · Build-vs-buy recommendation
- · Indicative roadmap & TCO
PACKAGE
Proof of Concept
For teams who need to validate a single use case end-to-end.
TIMELINE
4–8 wks
Fixed fee
TEAM
- ML architect
- 2 ML engineers
- Data scientist
OUTPUTS
- · Working PoC against measurable acceptance criteria
- · Evaluation harness
- · Cost / latency benchmark
- · Production-readiness gap analysis
PACKAGE · POPULAR
Production Build
For teams ready to ship.
TIMELINE
8–20 wks
Milestone-based
TEAM
- Architect · ML engineers
- MLOps · Frontend/Backend
- QA · PM
OUTPUTS
- · Production-deployed AI system
- · Guardrails, monitoring, MLOps
- · Integration with your stack
- · Deployment: on-prem · VPC · managed SaaS
PACKAGE
Managed AI Operations
For teams running AI in production needing senior operating partners.
TIMELINE
Ongoing
Fixed monthly
TEAM
- Dedicated SLA-backed pod
- Architect + MLOps + DS
OUTPUTS
- · Model monitoring & drift detection
- · Retraining cycles · safety reviews
- · Capability roadmap
- · Quarterly business reviews
PACKAGE
Mid-Training Observability Sprint
For teams running a pretraining or continued-pretraining job.
TIMELINE
2–4 wks
Fixed fee
TEAM
- ML architect
- 2 ML engineers
- Observability lead
OUTPUTS
- · Observability stack deployed (W&B / MLflow / on-prem)
- · Dashboards: loss, gradient norms, token efficiency, hardware
- · Early-stopping & rollback playbook
- · Live intervention support on your first instrumented run
PACKAGE
Post-Training Alignment Sprint
For teams with a fine-tuned base that needs to behave in production.
TIMELINE
4–6 wks
Fixed fee
TEAM
- Alignment lead
- 2 ML engineers
- Data ops
OUTPUTS
- · Preference data pipeline (collection + QA + reviewer agreement)
- · Reward model trained on your domain preferences
- · PPO / DPO / ORPO run against your fine-tuned base
- · Alignment delta report — before vs after, by axis
Fixed scope. Fixed timeline. Fixed fee. We refund the sprint if you don't get a written, decision-ready output.
SERVICE CATALOG
Seven capability areas.
Multimodal-first ordering. Each capability is a buildable scope under any of the six packages above.
A · MULTIMODAL
Multimodal & Vision-Language Model Services
VLMs are the convergence of the CV and LLM eras — and our strategic high ground. We build, fine-tune, and ship systems that see, read, and reason in one pass.
Talk to a specialist →VLM-based scene reasoning & visual Q&A
Multimodal agents (sees + reads + decides)
Image + text retrieval and grounding
Custom VLM fine-tuning for vertical domains
Multimodal evaluation, red-teaming, hallucination control
B · COMPUTER VISION
Computer Vision Services
CV is our DNA. PhotoCut processes 30M+ segmentations every month — that's the production baseline our enterprise CV runs at.
Talk to a specialist →Object detection, segmentation, instance tracking
Industrial defect inspection & quality control
OCR — printed, handwritten, vernacular scripts
Face / pose / gesture (privacy-preserving)
Edge deployment (mobile, embedded, on-device)
Video analytics and event detection
C · GENERATIVE VISUAL
Generative AI for Visual Content
LightX and Photoleaf generate 5M+ designs every month. We bring the same generation infrastructure — cost-tuned, brand-safe — to your catalog, ad, or creative ops stack.
Talk to a specialist →Text-to-image / text-to-video pipelines
Image editing, inpainting, background removal, relighting
Virtual try-on, product visualization, catalog automation
Style transfer · brand-consistent creative generation
High-volume generation infrastructure (cost / latency tuned)
D · VERNACULAR
Multilingual & Vernacular Intelligence
Proprietary OCR and NLU across 22 Indian languages and global scripts. Indic AI that actually deploys at population scale.
Talk to a specialist →SCRIPTS SUPPORTED
Indic-script text detection & recognition
Multilingual NLU, classification, sentiment
Vernacular content generation & translation
Cross-lingual search and retrieval
E · LLMS · RAG · AGENTS
LLMs, RAG & Agentic AI
Domain-fine-tuned LLMs deployed where you need them — including on-prem. Retrieval and agent runtimes that handle real enterprise traffic.
Talk to a specialist →Domain-specific LLM fine-tuning
LoRA · QLoRA · full SFT
RAG with hybrid retrieval & re-ranking
Qdrant · Weaviate · FAISS
Multi-agent workflows
LangGraph · custom orchestration
LLM evaluation, red-teaming, hallucination mitigation
Production deployment
vLLM · TGI · Triton · on-prem inference
F · NLP · DOCS
NLP & Document Intelligence
Extract structure from invoices, claims, contracts, and clinical reports. Enterprise search and Q&A over corpora that actually matter to your business.
Talk to a specialist →Document extraction
Invoices · contracts · claims · KYC
Named entity recognition
Domain vocabularies
Summarization · topic modeling · sentiment · conversation analytics
Enterprise search and Q&A over document corpora
G · SAFETY · MLOPS
AI Safety, Compliance & MLOps
Every production system we ship comes with guardrails, monitoring, and the compliance posture your industry requires.
Talk to a specialist →Guardrails
I/O validation · prompt-injection defense
Bias and fairness audits
Compliance alignment
GDPR · EU AI Act · India DPDP · HIPAA
SOC2-aligned MLOps · monitoring · drift detection
Human-in-the-loop and escalation workflows
TRAINING · ALIGNMENT · OPERATIONS
Training, Alignment & Operations
The disciplines that turn a capable model into a deployable one. Each sold standalone, each available as part of a Production Build or Managed Operations engagement.
H · TRAINING OBSERVABILITY
Mid-Training Observability & Intervention
training observability · w&b · mlflow · on-prem
Long pretraining and continued-pretraining runs fail silently. We instrument the run — gradient norms, token efficiency, hardware utilization, divergence detection — and intervene before the GPU bill outruns the experiment.
Talk to a specialist →Real-time training dashboards — loss curves, gradient norms, token efficiency, hardware utilization
Automated early stopping, checkpoint management, and rollback playbooks
Divergence and instability debugging for long runs
Cost / latency / accuracy trade-off monitoring during training, not just after
Integration with Weights & Biases, MLflow, or on-prem observability stacks
On-prem or VPC deployment — training telemetry never leaves your perimeter
I · ALIGNMENT
Post-Training Alignment & Optimization
rlhf · dpo · orpo · constitutional ai
A fine-tuned model knows your domain. An aligned model behaves the way your business needs it to — even on inputs it has never seen. We build the full alignment stack: preference data pipelines, reward models, and the optimization passes that turn a capable model into a deployable one.
Talk to a specialist →RLHF / RLAIF pipelines — preference data collection, reward modeling, PPO / DPO / ORPO
Constitutional AI and self-critique loops for safety-critical deployments
Multi-objective alignment — helpfulness, safety, brand voice, and compliance as joint objectives
Post-training compression — quantization-aware training, structured pruning, distillation, speculative decoding
Model merging and routing — SLERP, task arithmetic, mixture-of-experts routing applied post-training
Vertical alignment — legal, medical, financial, and Indic cultural alignment with domain-specific reward signals
J · CUSTOM MODELS
Full Custom Model Training Lifecycle
continued pretraining · synthetic data · slm
Some workloads can't be served by an API call or a LoRA on a frontier model. We run the entire training lifecycle — from raw corpus to deployed checkpoint — including the small specialized models that often out-perform much larger general ones in production.
Talk to a specialist →Continued pretraining on proprietary and domain corpora
Synthetic data generation pipelines (leveraging our GenAI stack) for fine-tuning and alignment data scarcity
Full training run management — data curation, tokenizer training, pretraining, fine-tuning, alignment, evaluation
Small specialized model (SLM) development — not just prompting or LoRA on big models
Sovereign and on-prem training infrastructure setup
K · HUMAN IN THE LOOP
Human-in-the-Loop Training & Preference Systems
hitl · preference data · continuous alignment
Alignment isn't a phase you finish. We build the human-in-the-loop platforms that make ongoing preference collection a routine operating discipline — not a project you re-run every year.
Talk to a specialist →End-to-end HITL platforms — preference collection UIs, annotation workflows, QA, reviewer agreement metrics
Integration with enterprise data labeling teams or synthetic preference generation via stronger teacher models
Continuous alignment baked into Managed AI Operations — preference data flowing back into reward models on a defined cadence
Reviewer disagreement analysis as a leading indicator of distribution shift
Multilingual and Indic-cultural preference workflows for vernacular deployments
L · CLOSED LOOP
Training-Eval-Deployment Closed Loop
drift signals · auto-retrain · lineage
Most MLOps stops at deploy. We close the loop: every drift signal, preference disagreement, and production failure flows back into the training data pipeline with full lineage, so the next checkpoint is informed by what production actually saw.
Talk to a specialist →Automated retraining triggers based on production drift and preference-signal thresholds
Training data versioning and lineage tied to production performance
"Training Readiness Assessment" as a Discovery / Consultancy sub-package
Eval harnesses that mirror production traffic, not synthetic benchmarks
Rollback infrastructure with checkpoint-level provenance
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