That Are Actually Ready for Production
We provide end-to-end LLM fine-tuning, RLHF alignment, and dataset preparation services. Tailored for enterprise teams, we transform generic foundation models into highly specialized, domain-specific AI. By rigorously preparing your proprietary data, we ensure your deployments are accurate, safe, and production-ready.
What We Offer
End-to-endmodeltrainingcapabilities—fromrawdatatodeployed,monitoredproductionmodels.
Supervised Fine-Tuning (SFT)
Hit your accuracy targets within your compute budget by instruction-tuning base models on proprietary data via LoRA, QLoRA, or full fine-tuning.
RLHF & Preference Alignment
Lock in your exact business rules, tone, and safety requirements—not just generic helpfulness—through custom reward modeling, RLHF, and DPO pipelines.
Dataset Preparation & Annotation
Turn raw, messy data into high-signal, training-ready datasets through expert sourcing, cleaning, deduplication, labeling, and synthetic generation.
Retrieval-Augmented Generation (RAG)
Eliminate hallucination without retraining by grounding model outputs in your proprietary knowledge base using custom retrieval pipelines and vector stores.
Model Evaluation & Red-Teaming
Prove your model is safe and production-ready before launch through custom eval harnesses, benchmark suites, adversarial red-teaming, and bias testing.
Deployment & MLOps
Keep your model performant and current post-launch with quantization, optimized inference serving, active drift monitoring, and continuous retraining pipelines.
Our LLM Capabilities
Deeptechnicalexpertiseacrosstraining,alignment,evaluation,anddeployment—everythingneededtotakeamodelfromprototypetoproduction.
Training & Alignment
- LoRA / QLoRA parameter-efficient fine-tuning
- Full fine-tuning for maximum performance
- Instruction dataset curation & templating
- Synthetic data generation pipelines
- Domain-specific vocabulary injection
- Multi-task and multi-turn training
- Reward model training & preference collection
- DPO / RLHF alignment pipelines
Evaluation & Deployment
- Custom RAG pipelines with vector store integration
- Embedding model fine-tuning
- Automated eval harnesses & benchmark suites
- Adversarial red-teaming & safety testing
- Model quantization (GGUF, AWQ, GPTQ)
- vLLM / TensorRT-LLM serving optimization
- Continuous monitoring & drift detection
- Retraining cadence & CI/CD for models
Where We've Applied This
LLMtrainingandRAGsolutionstailoredtotheuniqueneedsofdifferentverticals.
Customer Support Automation
Stop relying on generic scripts. We train domain-tuned agents to resolve tickets using your exact product knowledge and brand tone.
Legal & Compliance Review
By fine-tuning models on firm-specific review standards, we automate the flagging of risky clauses and complex contract summarization.
Financial Research Copilots
Accelerate analyst workflows with custom RAG setups that ground answers directly in your proprietary market data and internal research.
Healthcare Documentation
Clinical note summarization and documentation support you can actually trust, backed by rigorous safety evaluations and compliance checks.
Internal Knowledge Assistants
Give your employees instant, sourced answers. We train models on your internal wikis, SOPs, and past tickets to cut through the noise.
Frontier Model Refinement
Build AI that actually solves hard problems. We deliver the specialized fine-tuning required for complex coding assistants, reasoning models, and refined AI avatars.
Backed by a Managed Workforce, Not a Freelancer Pool
Behindeveryfine-tuningandannotationprojectisamanageddeliveryteam.Werundatasetlabeling,review,andQAthroughastructured,in-houseworkforce,whichmeansconsistentquality,accountabletimelines,andtheabilitytoscaleupfastwhenyourprojectneedsit.
200+ Managed Resources
A dedicated, in-house workforce of 200+ annotators, reviewers, and ML engineers — not a revolving door of freelancers. Same team, rigorous oversight, zero excuses.
Ad-Hoc Delivery Support
Need to scale a dataset overnight or turn around an urgent fine-tuning cycle? Our managed capacity means we can flex up for ad-hoc, time-sensitive delivery without re-onboarding new vendors.
Multi-Level Quality Control
Every single dataset and model output survives a strict review hierarchy — annotator → reviewer → QA lead. We catch hallucinations and errors long before they hit production.
Lower Cost Than Freelancer Models
A managed, in-house team trained on your standards is inherently more efficient than stitching together anonymous freelancers. You get faster iteration cycles and superior results, for less.
How We Work
Aprovenprocessfromdiscoverytodeployment—transparent,collaborative,andengineeredforproduction-gradeAI.
Discovery & Data Audit
First, our ML engineers assess your use case, existing data assets, compute constraints, and success metrics to scope the optimal training approach.
Dataset Curation & Annotation
Next comes the ground truth. This phase delivers a clean, labeled, training-ready dataset by sourcing additional data or generating synthetic examples to cover edge cases.
Fine-Tuning & Alignment
With data in hand, training begins. The model undergoes SFT and targeted RLHF/DPO alignment cycles, iterating strictly against your defined quality benchmarks.
Evaluation & Red-Teaming
Before any deployment, the candidate model is aggressively stress-tested against custom eval suites, adversarial prompts, and bias checks to guarantee safety.
Deployment & Monitoring
Finally, the optimized model drops into your infrastructure alongside quantization, serving enhancements, active drift monitoring, and a continuous retraining schedule.
Results That Matter
Thetrackrecordwebringtoeverynewmodeldeployment.
Reduction in hallucination rate after domain-specific fine-tuning vs. base model
Faster time-to-deploy using reusable fine-tuning and eval pipelines
Task-relevant accuracy on custom benchmark suites post-alignment
Continuous monitoring pipelines to catch drift before it impacts users
Ready to Train a Model That
Actually Knows Your Business?
TalktoourMLteamaboutfine-tuning,alignment,orRAGforyourusecase.