Generative AI & Consulting
We engineer end-to-end Generative AI systems and provide strategic technical consulting to safely accelerate your digital transformation. Our services bridge the gap between initial conceptual architecture and production-grade deployment. We build autonomous AI agents, construct high-throughput Retrieval-Augmented Generation (RAG) pipelines, optimize vector search systems, and design scalable workflows. We build using high-performance backend development languages and frameworks including Python, FastAPI, Express.js, and NestJS. We specialize in API development and integration, creating secure, high-throughput RESTful and GraphQL APIs that sync data between frontends and backend services. We ensure all custom software projects deliver high reliability, clean architectural conventions, and detailed documentation. Every engagement includes 2 weeks of post-launch operational support at no extra cost to guarantee that your team is fully onboarded and any minor post-release issues are resolved immediately.
Key Features & Benefits
Technologies We Leverage
Frequently Asked Questions
Have questions about our generative ai & consulting service? Find answers to commonly asked questions below.
How do you prevent data leaks when integrating enterprise LLMs?
We build secure middleware with custom prompt guardrails and utilize private VPC/local models or enterprise agreements to ensure your data is never trained on.
What is your experience with RAG and semantic search?
We design high-throughput Retrieval-Augmented Generation (RAG) pipelines using LangChain, LlamaIndex, and vector databases like pgvector, Pinecone, or Qdrant.
How do you reduce hallucinations in Large Language Models?
We apply context injection via vector search, configure low temperatures, and implement validation libraries to check outputs.
Can you deploy open-source models on our local servers?
Yes, we set up local model environments using Ollama or vLLM on private GPU instances to keep data in-house.
How do you structure vector indexing in PostgreSQL?
We utilize the pgvector extension with HNSW indexes to allow rapid cosine similarity lookups over large document sets.
What is the benefit of autonomous multi-agent systems?
Multi-agent frameworks let different AI agents handle separate sub-tasks, communicating to solve complex workflows.
How do you measure and optimize API token costs?
We set up rate limits, implement semantic prompt caching, and choose cost-effective models for simpler tasks.
Is post-launch support included for LLM updates?
Yes, we offer 2 weeks of free post-launch support to adjust system prompts, monitor logs, and handle model endpoint changes.
Can you construct custom dataset parsers for RAG ingestion?
Yes, we build custom data ingestion pipelines to clean and parse formats like PDF, CSV, and docx into structured vector chunks.
How do you secure AI agent endpoints from prompt injection?
We implement validation layers to detect adversarial prompts and verify output formats using typed schemas.
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