We build generative AI systems that work in production — not just impressive prototypes. Custom LLMs, RAG pipelines, AI agents, and enterprise copilots that deliver measurable ROI.
150+
GenAI Engineers
80+
LLM Deployments
85%
Avg. Processing Speedup
99.1%
Accuracy on Benchmarks
Production-grade GenAI applications across the enterprise — each with proven outcomes.
Extract, summarize, classify, and answer questions from contracts, reports, manuals, and unstructured documents at scale.
85% faster processingAI assistants embedded in your existing tools — Slack, Teams, Salesforce, SAP — that answer questions using your proprietary data.
3x productivity boostCustom coding assistants trained on your codebase, coding standards, and internal APIs — beyond what generic tools can do.
40% faster developmentIntelligent virtual agents that handle complex queries, escalate intelligently, and personalize responses using customer history.
60% ticket deflectionAutomated generation of financial reports, compliance documents, marketing content, and product descriptions at scale.
90% time reductionSemantic search that understands natural language queries and surfaces the most relevant information from your entire knowledge base.
70% faster retrievalWeek 1–2
Week 2–4
Week 3–8
Week 6–10
GPT-4o
OpenAIReasoning & multimodal
Claude 3.5 Sonnet
AnthropicLong context & safety
Llama 3.1 405B
Meta (Open Source)Private deployment
Gemini 1.5 Pro
Google1M token context
Mistral Large
Mistral AICost-efficient EU hosting
Command R+
CohereEnterprise RAG focus
Challenge
A 500-attorney firm was spending 40% of paralegal time on manual contract review and risk identification.
Solution
Built a RAG-powered contract intelligence platform fine-tuned on 100K+ legal documents, identifying clauses, risks, and deviations from standard templates.
Results
Challenge
Equity research analysts were manually synthesizing information from earnings calls, filings, and news to produce investment theses.
Solution
Deployed an AI research copilot with real-time data ingestion, earnings call transcript analysis, and structured report generation using GPT-4 + RAG.
Results
A domain-specific fine-tuning project typically takes 4–8 weeks depending on data availability, model size, and evaluation criteria. We can deliver an initial proof of concept in as little as 2 weeks using our AftoAI™ accelerator platform.
RAG retrieves relevant documents at inference time — ideal for large, frequently-updated knowledge bases. Fine-tuning bakes domain knowledge into the model weights, better for consistent tone or specialized task behavior. Most enterprise deployments use a hybrid approach.
We work across the full LLM ecosystem including OpenAI GPT-4o, Anthropic Claude 3, Meta Llama 3, Mistral, Google Gemini, and open-source models. We select the right model based on your performance, cost, privacy, and compliance requirements.
Book a free 60-minute technical session with our GenAI architects.