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Outcomes we deliver
- 30–50% reduction in manual effort for repeatable processes
- Faster cycle times (hours → minutes) with auditable workflows
- Lower cost-to-serve through targeted automation and agentic operations
Core AI Services
AI Automation (Process Orchestration)
Automate repetitive, rules-based tasks across IT, operations, finance, and compliance using secure APIs and workflow engines. Reduce errors, improve SLAs, and free your team for higher-value work.
AI Agents (Secure, Goal-Driven Assistants)
Deploy AI Agents that can reason over policies, call tools and APIs, and escalate to humans when needed. Use cases: Tier-1 IT support, procurement intake, knowledge retrieval, identity verification, and compliance checks.
n8n Workflow Development (Low-Code Automation)
Design and harden n8n workflows to connect CRMs, ticketing systems, cloud services, and data stores. Add guardrails (auth, rate limits, replay protection), versioning, and observability to move from POC to production.
LLM Support & Optimization (Open & Closed Models)
Select, tune, and right-size models (OpenAI, Azure OpenAI, open-weight LLMs). Implement prompt patterns, evaluation harnesses, response caching, and safety filters to increase accuracy while controlling cost/latency.
Build secure RAG over your documents, wikis, and SOPs with embeddings, chunking, and vector search. Ship answers with citations and audit trails, so stakeholders can trust the output.
Document AI & Intelligent Intake
Automate OCR, classification, entity extraction, and validation for contracts, invoices, medical/maintenance records. Route clean data into ERP/CRM with human-in-the-loop review and exception queues.
Enterprise/Gov AI Chatbots
Launch branded, policy-aware assistants for internal support or public portals. Options for private VNet/VPC and Azure Government. Enforce retention policies, redaction, and access controls by role.
Cyber AI & Anomaly Detection
Productionize models with CI/CD, feature stores, drift detection, cost and latency observability. Implement RBAC, KMS, secret rotation, and change management aligned to enterprise controls.
MLOps / LLMOps (Deploy, Monitor, Govern)
Build secure RAG over your documents, wikis, and SOPs with embeddings, chunking, and vector search. Ship answers with citations and audit trails, so stakeholders can trust the output.
AI Governance, Risk & Compliance (GRC)
Operationalize NIST AI RMF; map to NIST 800-53/171 and HIPAA where applicable. Establish model cards, evaluation reports, data retention rules, and approval workflows to pass security reviews.
Architectures We Implement
Retrieval-Augmented Generation (RAG): Sources → Embed/Chunk → Vector DB | Query → Retrieve → Grounded Generation with citations | Guardrails: PII redaction, policy filters, evaluation.
AI Agents: Policy-aware planner → Tool/API calling → Memory/Context store | Human-in-the-loop escalation and audit logs | Safe-mode fallbacks and cost controls.
n8n Orchestration: Triggers (webhooks, schedules, events) → Nodes (APIs, DBs, LLMs) | Error handling, retries, idempotency, and versioned releases
AI Implementation for Companies (Roadmap)
- Assess (1–2 weeks) – Process mapping, data readiness, security posture, success metrics.
- Pilot (4–8 weeks) – Build 1–2 high-impact use cases. Baseline vs. post-pilot time/cost.
- Productionize – Hardening (RBAC, logging, monitoring), cost/latency optimization, runbooks.
- Scale – Prioritized backlog, enablement, and ongoing LLMOps/GRC.
Deliverables: Solution design, security & compliance plan, pilot results dashboard, production runbook.
Secure-by-Design
- Access Control: SSO/RBAC, least privilege
- Data Protection: Encryption in transit/at rest, tokenization/redaction
- Isolation: Private networking/VPC/VNet options, managed secrets
- Observability: Cost, latency, accuracy, and drift dashboards
- Auditability: Prompt/response logging, model cards, evaluation reports
Compliance & Governance
- Frameworks: NIST AI RMF; mappings to NIST 800-53/171; HIPAA (if applicable)
- Policies: Data retention/deletion, PII redaction, incident response
- Documentation: Model cards, evaluation suites, DUA/BAA support (if applicable)
FAQs
What’s the fastest path to ROI with AI?
Pick one measurable, high-volume workflow. Run a 4–8 week pilot. Compare baseline vs. post-automation time and error rates to prove value before scaling.
Can you deploy in a private cloud or Azure Government?
Yes. We support private VPC/VNet deployments and Azure Government options with strict data residency and logging policies.
How do you keep data secure in LLM workflows?
RBAC, encryption, secrets management, PII redaction, prompt/response logging with retention controls, and human-in-the-loop for sensitive actions.
Build vs. buy—how do we decide?
We use a decision rubric (IP sensitivity, speed, TCO, data gravity, talent availability). Many clients start with low-code/orchestration and graduate to custom services where needed.
What does “AI Agents” mean in practice?
Policy-aware assistants that can read context, call tools/APIs, and pursue goals. They escalate to humans on confidence thresholds or risk triggers, and every action is logged