IbhanAi Labs · Engineering
AI engineered
The engineering behind every AI we ship — from training and hardening models, to architecting the infrastructure that serves them at scale, to deploying the inference and agentic systems that put AI to work in your business.
Capabilities
What we engineer
Ten areas of AI engineering, applied end-to-end — from measuring model quality to architecting the infrastructure that serves models at scale, then deploying inference and agentic systems into your environment.
LLM Evaluation & Benchmarking
Know how a model behaves before it ships.
- Evaluation harnesses & task suites
- Quality and safety benchmarks
- Regression tests across model versions
Training & Fine-tuning
Adapt open models to your data and your task.
- Domain fine-tuning & instruction tuning
- Distillation into smaller models
- Dataset curation from your sources
Optimization & Compression
Make models small and fast enough to run anywhere.
- Quantization & pruning
- On-device and edge optimization
- Latency and memory profiling
AI Infrastructure at Scale
Architect the systems that actually run AI in production.
- GPU / accelerator clusters & scheduling
- Distributed training & high-throughput inference
- Serving stacks — vLLM, Triton, Kubernetes
- AWS & GCP cloud, or your own data center
Observability & Monitoring
Keep deployed models healthy in production.
- Inference tracing & logging
- Drift and quality tracking
- Alerting on regressions
Multi-Modal & Computer Vision
For the problems that go beyond text.
- Vision and image understanding
- Speech and audio models
- Combined multi-modal pipelines
Retrieval & Agents
Ground models in your own knowledge and tools.
- Private retrieval over your data
- Tool-using, multi-step agents
- Model Context Protocol integrations
Safety & Guardrails
Keep private AI inside your rules.
- Policy enforcement & content filters
- Alignment and refusal tuning
- Red-teaming and abuse testing
Data & Synthetic Data
The fuel behind evaluation and training.
- Curation and cleaning
- Labelling and annotation
- Synthetic data generation
Model Serving & MLOps
The bridge from lab bench to your ground.
- Serving in your own network
- Autoscaling and cost control
- Model CI/CD and versioning
The full arc
From model to business impact
Four stages, run end-to-end — the piece in the middle, infrastructure at scale, is what turns a working model into a system your business can actually rely on.
Train & Optimize
Evaluate, fine-tune, distill and compress models to fit your task, your data and your hardware.
Architect Infrastructure
Design the clusters, serving stack and pipelines that run models reliably and cost-effectively at scale.
Deploy Inference & Agents
Ship inference and tool-using agentic systems into your own environment, with guardrails and observability.
Deliver AI for Business
Turn it into products and industry solutions that move real business metrics.
Work with us
Have a model or a problem to take through the cycle?
Tell us where you are — a model to evaluate, data to fine-tune on, or something to run in your own network — and we'll scope a focused pilot.