AI Development Services
Stop building "Toy AI". Professional AI development services to engineer production-grade RAG systems that don't hallucinate, using your own data.
⚠️ The Hallucination Trap
Training an LLM on your data is expensive and slow. When the data changes, the model is obsolete. The solution is RAG (Retrieval Augmented Generation), which connects a frozen LLM to your live data.
+ RAG vs Fine-Tuning:
Top AI Development Services Companies
| Company | Specialty | Cost | Our Rating ↓ | Case Studies |
|---|---|---|---|---|
| Databricks PS | GenAI & LLMOps | $$$$ | ★4.8 | 500 |
| Weights & Biases | MLOps Platform | $$ | ★4.8 | 150 |
| Quantiphi | AI-First Engineering | $$$ | ★4.7 | 42 |
| Scale AI | Data Labeling & RLHF | $$$$ | ★4.7 | 80 |
| Hugging Face Expert Services | Open-Source LLM Deployment | $$$ | ★4.7 | 45 |
| Deloitte AI | Governance & Strategy | $$$$ | ★4.6 | 120 |
| Anyscale | Ray & Distributed ML | $$$ | ★4.5 | 32 |
| Credo AI | Responsible AI & Governance | $$ | ★4.4 | 18 |
| Tredence | Last Mile AI Adoption | $$$ | ★4.3 | 29 |
| Fractal | Advanced Analytics | $$$$ | ★4.3 | 60 |
Databricks PS
GenAI & LLMOps
Weights & Biases
MLOps Platform
AI-First Engineering
Scale AI
Data Labeling & RLHF
Open-Source LLM Deployment
Deloitte AI
Governance & Strategy
Ray & Distributed ML
Responsible AI & Governance
Tredence
Last Mile AI Adoption
Fractal
Advanced Analytics
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True Cost of Enterprise AI
* RAG is the most cost-effective path for 90% of use cases. Custom training is reserved for massive datasets (1TB+) and specialized domains.
LLM Market Share 2025
The AI Hierarchy of Needs
You cannot jump straight to "Generative AI" if your foundation is broken. Most organizations are stuck at the bottom, trying to buy their way to the top.
Why Projects Fail:
- ❌ The "Magic Wand" Fallacy: Buying Copilot/ChatGPT Enterprise without fixing the underlying data permissions. Result: Interns accessing CEO salaries.
- ❌ The "Data Swamp": Dumping everything into S3 without a catalog. Result: Data Scientists spend 80% of time cleaning data.
- ✅ The Solution: Treat data as a product. Assign owners. Measure quality scores. Only then, apply AI.
AI Development Services
Professional AI development for RAG Architecture, LLMOps, and AI Readiness.
Enterprise AI Readiness Assessment
Audit your data quality before you spend $1M on GenAI. 77% of data is not ready.
RAG Architecture Design
Prevent hallucinations by grounding LLMs in your own data. Vector DB implementation guide.
AI Development Migration Guides
Moving from Rule-Based Systems to ML Models.
Mainframe to AWS
→Moving data out of AWS back to on-premise systems (for hybrid scenarios) can be incredibly expensive. Architect your VPC endpoints and Direct Connect links carefully to minimize traversal costs.
Mainframe (z/OS) to Azure
→Mainframe security (RACF/TopSecret) is granular and dataset-based. Azure Entra ID (Active Directory) is role-based. Mapping these permissions without opening security holes is a major audit risk.
AI Development Services FAQ
Q1 Why do 95% of GenAI projects fail?
Data Quality. 77% of organizations rate their data quality as 'average or worse'. If you feed a Large Language Model (LLM) dirty, inconsistent, or siloed data, it will 'hallucinate' plausible but wrong answers. You cannot build an AI strategy on a data swamp. Fix the data foundation first.
Q2 RAG vs Fine-Tuning: Which should we use?
For 90% of enterprise use cases, Retrieval Augmented Generation (RAG) is superior. It's cheaper, faster to update (just update the database), and less prone to hallucinations because you can cite sources. Fine-tuning is only necessary if you need the model to learn a new language, style, or highly specific medical/legal nomenclature.
Q3 How much does an enterprise AI project cost?
A production-grade RAG pilot typically costs $150K-$300K. This includes data engineering, vector database setup, and prompt engineering. Ongoing costs are dominated by token usage (API fees) or GPU hosting (if self-hosted). Budget $50K/year for maintenance and model updates.
Q4 Is my data safe with OpenAI?
If you use the Enterprise API, yes. OpenAI does not train on API data by default. However, for highly regulated industries (Healthcare, Defense), self-hosting open-source models (like Llama 3) on your own VPC (via Bedrock or Azure) is the safest path to ensure zero data leakage.
Q5 What is an AI Agent?
An AI Agent is an LLM that can take action, not just talk. Instead of just summarizing a report, an Agent can read the report, open Jira, create a ticket, and Slack the project manager. This requires a complex orchestration layer (like LangChain) and rigorous guardrails to prevent unintended actions.