Custom LLM Fine-Tuning
Domain-specific language models trained on your proprietary data for expert-level AI responses.
Applied intelligence
AI that answers from your own data
Retrieval systems, fine-tuned models and predictive analytics built on your documents and your history — deployed through an API, your private cloud, or entirely on your own hardware.
< 200ms
Inference target
3 modes
Deployment options
Cited
Answer sources
45%
Ticket drop, CloudServe
What we build
Models that do a specific job
Four families of applied AI. Most engagements start with retrieval, because it delivers value without needing training data.
RAG Architecture
Retrieval-Augmented Generation systems giving your teams instant, accurate answers from internal knowledge bases.
Predictive Analytics
ML models identifying demand trends, churn signals, and operational anomalies weeks before they surface.
Computer Vision
Real-time object detection, defect recognition, and visual inspection systems for industrial automation.
Python / PyTorchOpenAI APILangChainTensorFlowHugging FaceAWS SageMaker
Data boundary
Three places your AI can run
The first question worth settling is not which model — it is where your data is allowed to go. Everything else follows from that.
Option A
Enterprise API
Commercial models under a zero-retention agreement. Fastest to production and strongest raw capability.
Option B
Private cloud
Open-weight models (Llama, Mistral) running in your own cloud tenancy under your billing and access control.
Option C
Fully on-premise
Models served on hardware you own, air-gapped where required. Nothing crosses your network boundary at all.
Most clients over-restrict at first and pay for it in capability. If your data is not actually regulated, a zero-retention enterprise API gets you a better system in days. We help you decide that honestly rather than defaulting to the most expensive option.
Fit assessment
What AI is actually good at
Where machine learning genuinely returns, and where it does not. We would rather tell you before the invoice than after.
Document Q&A
Staff asking questions of large internal knowledge bases instead of searching folders.
Strong fitClassification & routing
Tickets, emails or documents sorted automatically into the right queue.
Strong fitDemand forecasting
Inventory and staffing predictions from historical operational data.
Strong fitAnomaly detection
Flagging transactions or sensor readings that deviate from learned norms.
Strong fitVisual inspection
Defect identification on production lines from camera feeds.
Needs image dataOpen-ended generation
Unconstrained creative output with no verifiable correct answer.
Weak fitHow we work
What AI vendors rarely admit
Your data quality sets the ceiling, not the model. Teams arrive asking which model to use when the real constraint is that the labels are inconsistent or the documents are five years stale. That is why we sell a feasibility audit first — it is cheap, and it occasionally ends the conversation honestly.
Retrieval beats fine-tuning far more often than it is sold. Fine-tuning teaches a model a style; retrieval gives it facts. If the goal is accurate answers from your documentation, RAG is cheaper, updates instantly when a document changes, and can cite its sources — which fine-tuning cannot.
Evaluation is the deliverable people forget to ask for. A demo on five hand-picked questions proves nothing. Every system we ship comes with a held-out test set and real precision and recall numbers, because that is the only way to know whether the next change made it better or worse.
Models decay quietly. Accuracy does not fail loudly — it drifts as your data shifts, and nobody notices until a decision is visibly wrong. Drift monitoring and a retraining trigger belong in the initial scope, not in a follow-up project after something has already gone wrong.
Build standard
Defaults on every model
Private & Secure
Zero-retention enterprise APIs and on-premise model options ensure your data never trains public models.
Sub-200ms Latency
Optimized inference pipelines with quantization and caching delivering lightning-fast response times.
Explainable AI
SHAP-value attribution and decision path logging ensuring all model outputs remain auditable.
Legacy Integration
AI capabilities injected into existing software via REST APIs — no full platform rewrite required.
Process
Data audit to production
01
Data Assessment
Auditing your existing data quality, volume, and structure to determine the optimal AI approach.
02
Model Selection
Choosing between fine-tuned open-source models or commercial APIs based on your latency and privacy requirements.
03
Training & Validation
Iterative model training with rigorous evaluation metrics including precision, recall, and F1 scoring.
04
Production Deployment
Containerized model serving with auto-scaling, A/B testing infrastructure, and continuous monitoring.
In their words
Results from a live system
Oacer built us a custom RAG system over our 50,000-document knowledge base. Our support ticket volume dropped 45% in the first month — the AI answers questions better than our junior staff.
Deepak SharmaVP of Product, CloudServe SaaS Platform
Engagement
How AI projects are priced
Indicative ranges. Inference running costs are projected separately during feasibility, since they scale with usage rather than being a one-off.
Feasibility
Data & viability audit
From ₹1,50,000 ≈ $1.8k / £1.4k
A short, fixed engagement answering whether your data can support the outcome you want — before anyone commits to a build.
Data quality & volume audit
Model approach recommendation
Cost & latency projection
Written go / no-go finding
Build
RAG or custom model
₹8L – ₹30L+ ≈ $9.6k – $36k+
A production system: ingestion pipeline, model, evaluation harness, API and deployment into your environment.
Ingestion & embedding pipeline
Model selection or fine-tuning
Evaluation harness with metrics
API + deployment to your cloud
MLOps
Monitor & retrain
Monthly Scoped per model
Models decay as your data shifts. This keeps accuracy where it was on launch day rather than quietly drifting.
Data drift detection
Accuracy degradation alerts
Scheduled retraining pipeline
Cost & latency optimisation
Where we work
Six markets, local data rules
Data residency and deployment mode are decided per client and per jurisdiction, at design time.
India
Hyderabad-based ML team, with on-premise deployment available for clients with data-localisation requirements.
United States
HIPAA-conscious architecture for health data, and zero-retention agreements documented for procurement review.
United Kingdom
GDPR-aligned pipelines with lawful-basis and retention decisions made at design time, not retrofitted.
UAE
In-region deployment where localisation rules apply, plus Arabic-language retrieval and evaluation.
Australia
Near-full working-day overlap with IST and ap-southeast-2 hosting for latency-sensitive inference.
New Zealand
Async delivery with written evaluation reports each sprint, so model quality is reviewable without a call.
FAQ
AI & machine learning questions
Absolutely. We offer three deployment options: enterprise OpenAI API with zero-training retention, private cloud deployment using open-source models (Llama, Mistral), or fully on-premise installation on your own servers.
Next step
Find out if your data can support it
Tell us the decision you want automated and what data sits behind it. The feasibility audit answers whether it is viable — including when the answer is no.
Typical reply within one business day · findings shared whether or not you proceed