We design production-grade generative AI systems, from custom LLM fine-tuning on your proprietary data to multi-modal pipelines, RAG architectures, and responsible AI governance, built for enterprise scale and federal compliance.
The generation of AI that merely answered questions is over. Modern enterprise AI must reason over private data, take multi-step actions, operate safely within compliance boundaries, and deliver measurable ROI, not demos.
Softcom's AI & ML practice combines deep ML engineering with production systems expertise. We implement Retrieval-Augmented Generation (RAG) with semantic chunking and hybrid search, fine-tune foundation models via LoRA / QLoRA on client-specific corpora, and orchestrate complex multi-step reasoning with LangGraph stateful agent graphs.
Key differentiator: We evaluate every AI system before production using frameworks like RAGAS (context recall, answer faithfulness, context precision) and LangSmith tracing, so you know exactly what works and why.
A breakdown of the specific tools, patterns, and practices we bring to every AI engagement.
We implement advanced RAG pipelines with semantic chunking (recursive, sentence-window, hierarchical), hybrid BM25 + dense retrieval, and re-ranking with Cohere Rerank v3 or cross-encoders. Evaluated end-to-end with RAGAS context precision and answer faithfulness metrics.
Custom model adaptation using LoRA and QLoRA (4-bit quantized) for domain-specific tasks, including legal document analysis, clinical note coding, and federal regulation Q&A. Full RLHF workflows with reward models and PPO optimization. Constitutional AI alignment for high-stakes deployments.
Stateful multi-step agent graphs using LangGraph with human-in-the-loop checkpoints, parallel node execution, and conditional branching. LangChain v0.3 LCEL expression language for composable retrieval chains. LangSmith for production tracing and regression testing.
Integrate vision, audio, and document modalities with Claude 3.5 Sonnet (vision + long context), GPT-4o (vision + function calling), and Gemini 2.0 Flash (native audio/video). Use cases include visual document extraction, diagram interpretation, and audio-to-insight pipelines.
Systematic prompt optimization with chain-of-thought, few-shot, and self-consistency techniques. Enforced structured outputs via JSON Schema (OpenAI), instructor library with Pydantic validators, and tool/function calling patterns for reliable data extraction from unstructured text.
Constitutional AI principles, guardrail frameworks (NeMo Guardrails, Guardrails AI), bias detection in training data, PII scrubbing pipelines, adversarial prompt red-teaming, and federal AI governance per NIST AI RMF 1.0 and EO 14110 requirements.
Intelligent model routing with LiteLLM sends simple queries to Llama 3.3 at $0.002/1M tokens and complex reasoning to GPT-4o. Prompt caching (Anthropic + OpenAI) for repeated context blocks cuts costs up to 90%. Token counting, budget alerts, and usage dashboards in LangSmith.
End-to-end evaluation before production using RAGAS (answer faithfulness, context recall, context precision, answer relevance), LlamaIndex evaluation modules, and Promptfoo for regression testing across prompt versions. Automated eval pipelines in CI/CD with threshold gates.
Deploy quantized models (GGUF/GGML via llama.cpp, ONNX Runtime) at the edge for air-gapped federal environments. Server-sent events (SSE) streaming for real-time UI responsiveness. vLLM for high-throughput batched inference on-premise with PagedAttention.
Every engagement begins with use-case qualification to ensure AI is the right tool. We then architect the minimal viable pipeline, evaluate it rigorously, and expand only what delivers verified value.
Our delivery teams include AI engineers, ML researchers, data engineers, and security architects working in 2-week sprints with weekly demos and measurable eval checkpoints.
We interview stakeholders, audit existing data assets, and evaluate AI fit. Not every problem needs an LLM; we identify whether RAG, fine-tuning, a classical ML model, or a deterministic system is the right approach. Output: prioritized use-case backlog with ROI estimates.
We ingest, clean, and chunk your proprietary documents (PDFs, databases, SharePoint, Confluence, S3) into vector-ready embeddings. We test multiple embedding models (text-embedding-3-large vs. BGE-M3) and chunk strategies to maximize retrieval precision before any LLM call.
We select the optimal RAG pattern (naive, advanced, modular, graph-based), orchestration framework (LangGraph for stateful agents, LlamaIndex for complex retrieval), and foundation model. A working prototype with a RAGAS baseline is delivered within 2 weeks.
We build a golden dataset of representative Q&A pairs and run RAGAS metrics after every iteration. Guardrails (NeMo or Guardrails.ai) are integrated. LangSmith tracing captures every LLM call, token cost, and latency measurement in production-equivalent conditions.
Containerized deployment (Docker + Kubernetes) with autoscaling. LiteLLM router for model fallback and cost routing. Prompt caching enabled on eligible models. API rate limiting, PII redaction middleware, and audit logging. Monthly cost reports with optimization recommendations.
Ongoing model drift detection, human feedback collection (thumbs up/down, corrections) fed back into fine-tuning pipelines. Federal deployments comply with NIST AI RMF 1.0, FISMA, and DoD AI Ethical Principles. Quarterly AI governance reviews with responsible AI scorecards.
Concrete examples of how generative AI is delivering measurable value across industries.
Built a RAG system over 40,000+ federal acquisition regulations (FAR, DFARS, agency supplements) using Pinecone Serverless + Claude 3.5 Sonnet. Contracting officers query complex regulatory questions in natural language. Hybrid BM25 + dense retrieval with Cohere re-ranking delivers authoritative, cited answers. RAGAS answer faithfulness: 97.3%.
73% faster regulatory researchFine-tuned Llama 3.3 70B using QLoRA on 2M+ clinical notes to predict ICD-10-CM codes. Achieved 91.4% coding accuracy vs. 78% for zero-shot GPT-4o, at 95% lower inference cost. HITL workflow routes low-confidence predictions to human coders. Fully HIPAA-compliant on-premise deployment using vLLM.
91.4% coding accuracy at 95% cost savingsDeployed a multi-modal knowledge assistant for a Fortune 500 professional services firm, ingesting PDFs, PowerPoints, meeting recordings, and SharePoint wikis. GPT-4o Vision extracts insights from charts and diagrams. LangGraph orchestrates document retrieval + calendar lookup + Slack posting in a single workflow. 12,000+ daily active users within 60 days of launch.
12,000 daily users, 4.6/5 satisfactionConducted adversarial red-teaming of a client's internally deployed LLM-based HR system before production release. Used Promptfoo with 500+ attack prompts (jailbreaks, prompt injection, PII extraction attempts, role-play attacks). Identified 23 critical vulnerabilities, implemented NeMo Guardrails, and re-tested to zero critical findings. Delivered full NIST AI RMF 1.0 risk assessment report.
23 vulnerabilities found & remediated pre-launchStart with a 2-week AI Discovery Workshop: we assess your data, qualify use cases, and deliver a prioritized AI roadmap with ROI projections.