10decodersBuild AI that
actually works for your business.
From knowledge bases and agentic workflows to production-grade digital workers — 10decoders helps enterprises deploy AI that's safe, measurable, and built to last.
Trusted by leading enterprises and healthcare teams
Three levels. One path forward.
Enterprise AI doesn't start with agents. It starts with clean data, then layered intelligence, then autonomous operation. We run each engagement through the right level for your actual maturity — not the one that sells the most hours.
Knowledge Base with RAG
Build a co-pilot or enterprise knowledge layer using Retrieval-Augmented Generation. Standalone, or integrated with Microsoft Copilot, Salesforce, Shopify, or your existing stack.
- 2+ hours recovered per employee daily
- Static documents become a live intelligence engine
- Connects to existing apps — no rip-and-replace
AI Workflows & Automation
Sequence agentic workflows using CrewAI and similar frameworks. Automate end-to-end processes across departments — from intake to decision to delivery — without handoff bottlenecks.
- 30–50% cut in infrastructure waste
- 80% reduction in document processing time
- Revenue-cycle actions run autonomously
Digital Workers
Deploy 24/7 autonomous agents that handle background tasks and fully automate business processes. Revenue catalysts that never sleep — upselling, engaging, processing, reporting.
- 90% reduction in interaction costs
- 6–10% revenue lift through autonomous engagement
- Scales with workload, not headcount
Not there yet? That's exactly who this is for.
Every engagement starts from where you actually are. Here's what we hear most — and where each one leads.
What we build for you.
Our AI services span the full stack — from data readiness through to live agentic deployment. Each engagement is scoped against your AI maturity, not templated to a vendor roadmap.
AI Data Cleanup Agency
Stop the data tax. Recover 12.5% of staff time by cleaning, structuring, and unlocking dormant capital in your enterprise data estate.
Zero-Trust Security Layer
Mitigate breach exposure while expanding valuation multiples through defensible AI IP assets. Security-by-design, not security-by-afterthought.
Autonomous Agent Build Agency
Full-lifecycle agentic AI build — from architecture through production deployment. QA, data foundation, and trust layer included from day one.
Enterprise Knowledge Bases
Turn static documents into high-speed intelligence engines. RAG-powered search and synthesis, deployed into your existing ecosystem in weeks.
AI Workflows & Automation
Cut infrastructure waste and eliminate document processing bottlenecks. Process automation that compounds — not cron jobs relabelled as AI.
Digital Workers (Autonomous Agents)
Revenue-generating, 24/7 autonomous agents that handle background operations, upselling, and customer engagement without human intervention.
Ready-to-deploy AI agents.
Technical and domain-specific agents — built on our Rapid Agent Builder framework with data foundation, QE validation, and trust layer included.
Design QA Agent
Compares Figma designs with live pages — catches inconsistencies before users do.
QA Automation Agent
Automates Jira status transitions for design and QA — no manual follow-ups.
Performance Optimizing Agent
Evaluates system performance under load — catches bottlenecks early.
EMR Copilot
Real-time clinical insights from patient records — faster decisions, less documentation.
CyberSecurity Agent
Monitors CVE feeds, identifies applicable risks, delivers executive-ready alerts.
HIPAA Compliance Agent
Evaluates HIPAA readiness, delivers a compliance report with clear next steps.
Med Secure AI Agent
Reviews partner documents, scores risk, verifies healthcare compliance instantly.
Document Review Agent
Extracts and verifies data from contracts, invoices, and filings via OCR + NLP.
Credit Risk Validation Agent
Real-time risk scoring on applicant data — cycles from days to minutes.
ServiceDesk Automation Agent
24/7 first-line triage — resolves common issues, routes complex cases.
Meet our platforms.
Productized AI accelerators that cut time-to-pilot by months. Not integrations — proprietary platforms with domain IP baked in.
Move beyond simple search to deep document understanding. Extract, sequence, and analyze complex data across massive repositories — automating workflows that once required full analyst teams.
An advanced AI/ML platform with three specialized layers: CheiAI Data (document mining), CheiAI Vision (video + image AI), and Custom Models. Deployed on AWS with clinical-grade precision.
Transform visual data into actionable intelligence. Autonomous computer vision that monitors, detects, and reports on real-time events — safety violations, compliance breaches, operational anomalies.
Data foundations, zero-copy trust environment, and scalable business agents — packaged for SMBs that need enterprise-grade AI without enterprise-grade overhead. From data to decisions in 2 weeks.
Start where you actually are.
Data cleanup & foundations
Clean data, cloud connectors, ETL analytics. The prerequisite for everything that follows.
Unified data + AI enablement
Unified data platform, data migration, and AI integration into existing apps via RAG.
Production-grade AI at scale
ML Ops, agentic workflows, autonomous digital workers. AI that compounds.
How a typical engagement runs.
Discovery Workshop
Engage stakeholders, prioritize IT needs, define assessment scope and AI maturity position.
Pilot Implementation
Build QA agents, data foundation, and first agentic workflows. Measure and refine.
Full-Scale Enterprise AI
Deploy at scale with Agent Builder, production RAG pipelines, and ongoing AI Ops.
Deployed. Proven. Measured.
Real engagements, real results. These aren't demos — they're production AI systems running in enterprises today.
Enterprise AI, explained.
Honest answers about what works, what doesn’t, and how to get started without the hype.
What’s the difference between a knowledge base/RAG system, an AI workflow, and an autonomous digital worker?
These are three maturity levels, not synonyms. A RAG-based knowledge base retrieves and answers questions grounded in your documents. An AI workflow sequences multiple steps across a process end to end using orchestration frameworks like CrewAI. A digital worker is a 24/7 autonomous agent executing tasks with human oversight only where risk requires it.
Why do most enterprise AI pilots fail to reach production?
The most common cause is starting at the wrong maturity level — attempting agents before the underlying data is clean and connected. Fragmented data across silos makes AI outputs unreliable and erodes trust before the pilot proves value. Cleaning and structuring data should be the first step, not an afterthought.
What ROI can realistically be expected from enterprise AI deployment?
Outcomes vary by maturity level. Knowledge-base/RAG deployments have recovered roughly two hours of productive time per employee daily. Workflow automation has cut infrastructure waste 30–50% and document-processing time up to 80%. Full agentic builds report first-year ROI around 138%. These are representative outcomes, not guarantees.
Do we need to modernize legacy systems before starting AI initiatives?
Not as a hard prerequisite. 10decoders builds AI around legacy systems rather than through them, so modernization and AI work can run in parallel. Typically, an initial data-foundation layer connects to the legacy system as-is while a longer modernization roadmap proceeds alongside it.
How is a domain-specific AI agent different from a generic chatbot?
Generic chatbots are thin wrappers around a model with limited grounding. Domain agents, such as a HIPAA Compliance Agent or Credit Risk Validation Agent, are built on a shared framework bundling data-foundation work, QE validation, and a trust layer, so outputs are explainable and auditable — critical in regulated contexts.
How is enterprise AI data secured?
The AI practice includes a Zero-Trust Security Layer, positioned as security-by-design rather than a bolt-on control. This pairs with the firm’s ISO 27001 certification and, for healthcare, HIPAA-aligned controls with a signed BAA before data access. On Microsoft-based deployments, data stays within the client’s own Azure tenant.
