Enterprise-Grade AI Engineering Services
Build the future with our managed AI services. We design, build, and scale intelligent applications using Generative AI, LLMs, and autonomous agents.
From Hype to Impact
Stop Experimenting. Start Scaling.
Generative AI is transforming every industry, but moving from a prototype to a reliable production application is hard. Our AI Engineering Services bridge the gap, helping you build systems that are accurate, secure, and scalable.
- Eliminate hallucinations with grounded RAG
- Ensure enterprise-grade security & privacy
- Optimize costs and latency
- Integrate seamlessly with existing workflows
Our Capabilities
End-to-End AI Solutions
We handle the entire AI lifecycle, from model selection to production deployment.
LLM App Development
Build intelligent applications powered by GPT-4, Claude, and Llama. We create custom chatbots, copilots, and assistants tailored to your business needs.
RAG Systems
Connect LLMs to your private data. We build high-performance Retrieval-Augmented Generation systems using vector databases like Pinecone and Weaviate.
AI Agents & Automation
Go beyond chat. We build autonomous agents that can plan, reason, and execute complex workflows to automate business processes.
Fine-Tuning & SLMs
Optimize performance and cost. We fine-tune open-source Small Language Models (SLMs) on your proprietary data for specialized tasks.
AI Governance & Eval
Deploy with confidence. We implement guardrails, evaluation frameworks, and red-teaming to ensure your AI is safe, accurate, and compliant.
MLOps & Infrastructure
Scale your AI. We set up robust inference infrastructure, model monitoring, and CI/CD pipelines for your AI applications.
Why Partner with Salt for AI?
We combine cutting-edge AI research with rock-solid software engineering.
AI Native
We stay ahead of the curve. From the latest open-source models to agentic frameworks, we know what works and what's just hype.
Security First
We understand the risks of AI. We implement strict data governance, PII redaction, and secure deployment patterns to protect your IP.
Engineering Rigor
AI is software. We apply the same rigor—testing, version control, CI/CD, and observability—to our AI systems as we do to any mission-critical app.
Industries
AI Engineering Across Industries
We build tailored AI solutions that address specific industry challenges.
Healthcare
Automate patient intake, summarize medical records, and build HIPAA-compliant chatbots.
Fintech
Detect fraud in real-time, automate loan processing, and build personalized financial advisors.
SaaS
Add generative AI features to your product, from natural language search to automated content creation.
E-commerce
Create hyper-personalized shopping experiences and intelligent customer support agents.
Logistics
Optimize routes, predict maintenance needs, and automate supply chain documentation.
Legal & Professional
Automate contract review, summarize legal precedents, and draft documents faster.
Our Process
Our AI Engineering Methodology
A rigorous, scientific approach to building production-grade AI systems.
Data Curation & Strategy
AI is only as good as the data it feeds on. We start by auditing, cleaning, and structuring your proprietary data to ensure it's ready for RAG or fine-tuning.
Model Selection & Evaluation
We benchmark multiple models (GPT-4, Claude, Llama) against your specific use cases to find the optimal balance of performance, cost, and latency.
System Architecture & RAG
We design the retrieval systems, vector stores, and prompt chains. We implement advanced RAG techniques like hybrid search and re-ranking to minimize hallucinations.
Guardrails & Safety
We implement strict input/output validation to prevent prompt injection, toxic responses, and data leakage. Security is baked in, not bolted on.
Deployment & MLOps
We deploy the system with comprehensive monitoring. We track token usage, latency, and user feedback to continuously improve the system.
How We Engage
A proven process to take you from concept to production.
Discovery
We identify high-impact use cases and assess data readiness.
Prototype
We build a rapid proof-of-concept to validate feasibility.
Production
We harden the system with guardrails, eval, and robust infra.
Scale
We optimize for latency, cost, and user adoption.
The AI Tech Stack
We use best-in-class tools to build robust AI applications. Our stack is modular, allowing us to swap components as technology evolves.
Models (LLMs & SLMs)
OpenAI (GPT-4), Anthropic (Claude), Meta (Llama), Mistral.
Orchestration
LangChain, LlamaIndex, Vercel AI SDK.
Vector Databases
Pinecone, Weaviate, Qdrant, pgvector.
Our Toolchain
- OpenAI & Anthropic
- LangChain & LlamaIndex
- Pinecone & Weaviate
- LangSmith & Arize
Frequently Asked Questions
Common questions about our AI engineering services.
How is AI Engineering different from Data Science?
Data Science focuses on researching and training models. AI Engineering focuses on taking those models (or existing APIs like OpenAI) and building reliable, scalable production applications around them. We bridge the gap between a demo and enterprise software.
Can you help us build a private ChatGPT?
Yes. We can build a secure, private internal assistant that has access to your company's documents and data (via RAG) without sharing that data with public model providers for training.
Which LLM should we use?
It depends on your use case. For complex reasoning, GPT-4 or Claude 3.5 Sonnet might be best. For speed and cost, a smaller model like Llama 3 or Haiku might be better. We help you evaluate and select the right model mix.
How do you handle data privacy with AI?
We prioritize security. We use enterprise endpoints that do not train on your data. We also implement PII masking, RBAC, and secure vector stores to ensure sensitive information remains protected.
What is your experience with RAG?
We have deep expertise in building RAG systems. We handle the complexity of chunking strategies, embedding model selection, hybrid search (keyword + semantic), and re-ranking to ensure high retrieval accuracy.
How long does it take to build an AI MVP?
With our pre-built accelerators and SPARK framework, we can typically deliver a functional AI MVP in 4-6 weeks. This includes data preparation, model integration, and a production-ready UI.
Should we fine-tune a model or use RAG?
For most enterprise use cases involving knowledge retrieval, RAG is the better starting point as it's cheaper, faster, and reduces hallucinations. Fine-tuning is best reserved for teaching a model a specific style, format, or new language pattern.
Ready to Build Intelligent Apps?
Get a free AI readiness assessment and see how we can help you transform your business with Generative AI.
Expert Insights
AI Engineering Insights & Strategies
Learn how to build, deploy, and scale production-ready AI applications that drive real business value.
Table of Contents
What is AI Engineering?
AI Engineering is the discipline of making Artificial Intelligence work in the real world. While data science focuses on model creation and experimentation, AI Engineering focuses on the systems, tools, and processes required to deploy, monitor, and scale those models in production environments.
It bridges the gap between research and application. AI Engineering Services encompass everything from setting up vector databases and designing prompt chains to implementing guardrails and ensuring cost-effective inference.
In the era of Generative AI, this role has become critical. It's no longer just about training models; it's about orchestrating complex interactions between LLMs, data sources, and user interfaces.
The Role of an AI Engineer
An AI Engineer sits at the intersection of software engineering and data science. Unlike a pure data scientist who might focus on algorithm theory, an AI Engineer focuses on practical application.
Key responsibilities include:
- Model Deployment: Wrapping models in robust APIs (FastAPI, Flask) and deploying them to cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI).
- Inference Optimization: Reducing latency and cost by using techniques like quantization, distillation, and caching.
- System Integration: Connecting AI models to existing business applications, databases, and user interfaces.
Enterprise AI Strategy
Adopting AI in the enterprise requires more than just technical capability; it requires a strategic roadmap. Enterprise AI Solutions must be scalable, secure, and aligned with business goals.
We help organizations navigate:
- Build vs. Buy: Deciding when to use off-the-shelf APIs (like OpenAI) versus training custom open-source models (like Llama 3 or Mistral).
- Data Readiness: Assessing if your data infrastructure can support advanced AI applications like RAG.
- Change Management: Training your workforce to effectively use AI tools and copilots.
Generative AI & LLMs
Generative AI and Large Language Models (LLMs) like GPT-4, Claude, and Llama have revolutionized software development. They enable applications to understand, generate, and reason with human language at an unprecedented level.
However, integrating these models into enterprise applications requires more than just an API call. It involves:
- Prompt Engineering: Crafting and optimizing prompts to get consistent, high-quality outputs.
- Model Selection: Balancing performance, cost, and latency by choosing the right model for the task (e.g., GPT-4 for reasoning vs. Llama 3 for speed).
- Fine-Tuning: Adapting open-source models on your proprietary data to improve performance on specific domains.
RAG (Retrieval-Augmented Generation)
One of the biggest challenges with LLMs is hallucinations and lack of knowledge about your private data. Retrieval-Augmented Generation (RAG) solves this by connecting the LLM to your internal knowledge base.
A typical RAG architecture involves:
- Vector Database: Storing embeddings of your documents (PDFs, Wikis, Databases) in tools like Pinecone, Weaviate, or pgvector.
- Retrieval System: Semantically searching for relevant context based on the user's query.
- Generation: Feeding the retrieved context to the LLM to generate an accurate, grounded answer.
Our Data Engineering teams ensure your data is clean and structured, which is essential for high-quality RAG systems.
AI Agents & Copilots
Beyond simple Q&A, we are moving towards AI Agents—systems that can take action. An AI Agent can plan a workflow, use tools (like searching the web, querying a database, or calling an API), and execute tasks autonomously.
We build:
- Enterprise Copilots: Assistants that live inside your software to help users complete tasks faster (e.g., a coding assistant or a legal document drafter).
- Autonomous Agents: Background processes that can handle complex workflows like customer support triage or supply chain optimization.
Why Managed AI Services?
The AI landscape is moving at breakneck speed. New models and techniques emerge weekly. Keeping up requires a dedicated team that lives and breathes this technology.
With our Managed AI Engineering Services, you get:
- Rapid Prototyping: We use our proven accelerators to get you from idea to MVP in weeks, not months.
- Production Grade: We don't just build demos. We handle evaluation, observability, rate limiting, and security to ensure your AI app is enterprise-ready.
- Cost Optimization: We help you manage token usage and infrastructure costs, ensuring your AI ROI remains positive.
Ready to build the future? Contact us to start your AI journey.