AI Services

Identifying high-value AI use cases, data readiness, ROI analysis, and adoption roadmaps

Policies for ethical use, model risk management, and regulatory alignment (incl. data residency requirements)

Structured workshops with business units to identify and prioritize automation and AI opportunities

Purpose-built autonomous and semi-autonomous agents for specific business tasks (research, procurement, reporting, ticketing)

Designing teams of coordinated agents with frameworks such as LangGraph, CrewAI, AutoGen, and MCP-based tool integration

Fine-tuning LLMs and domain models on your own data — across commercial and open-source foundation models, deployed in your environment.

Training and deploying compact domain-specific models for cost-sensitive or on-premises use

Self-hosted model deployment for data-sovereignty and security-sensitive clients (GPU sizing, vLLM/Ollama, air-gapped setups)

Designing, testing, and versioning prompts and system instructions for reliability and cost efficiency

Branded chatbots trained on company knowledge for customer service, sales, HR, and internal helpdesk — web, WhatsApp, and in-app

Voice bots, speech-to-text/text-to-speech, AI call center agents, real-time transcription, and multilingual voice support (incl. Arabic)

Retrieval-augmented generation over company documents, wikis, and knowledge bases with vector databases (Pinecone, Weaviate, pgvector)

Content generation, summarization, and copilot-style assistants embedded into employee workflows

Embedding AI into Salesforce, Microsoft Dynamics 365, HubSpot, and Zoho: lead scoring, next-best-action, auto-summaries, and AI-assisted sales workflows

AI-enhanced processes in SAP, Oracle, and Dynamics: forecasting, invoice matching, and intelligent approvals

Building and integrating AI APIs into existing products and portals; API gateways, rate limiting, usage metering, and monetization

Designing complete AI-powered workflows for any business or project — from intake to output — using n8n, Zapier, Make, Power Automate, or custom orchestration

AI assistants inside Microsoft Teams, Slack, and SharePoint (meeting summaries, Q&A bots, document assistants)

Product recommendations, dynamic pricing, personalization, and AI-driven campaign optimization

AI-driven monitoring, anomaly detection, and root-cause analysis using platforms such as Cisco DNA/Catalyst Center AI, Juniper Mist AI, and Aruba Central AIOps

Forecasting capacity, congestion, and failures from telemetry data before they impact users

Policy-driven automation that detects drift and auto-remediates configuration and performance issues

Using AI to accelerate HLD/LLD production, config generation, and network documentation (links to our Quick Services)

Designing and deploying AI-ready infrastructure: GPU clusters, high-speed fabrics (InfiniBand/RoCE), storage for AI, and data center readiness for AI workloads

Intelligent event correlation, alert noise reduction, and automated incident response across servers and applications

Automated runbooks triggered by AI detection: service restarts, scaling actions, and patch orchestration

AI-assisted IaC generation and validation (Terraform, Ansible) and configuration compliance checking

Auto-triage, ticket classification, suggested resolutions, and knowledge-base generation for ITSM platforms (ServiceNow, Jira SM)

Predictive models for forecasting, churn, risk scoring, and recommendations

Image/video analytics, object detection, OCR, quality inspection, and safety monitoring

Text classification, sentiment analysis, entity extraction, and translation (incl. Arabic NLP)

Model pipelines, evaluation, monitoring, guardrails, and productionization (SageMaker, Azure ML, Vertex AI, MLflow)

Standing up enterprise AI platforms with security and governance — spanning major cloud AI services and self-hosted, open-source LLMs.

Migrating between AI providers or model versions: prompt porting, evaluation benchmarking, and cost/performance comparison

Upgrading rule-based RPA and scripts to AI-driven automation and agents

Moving and restructuring data into lakes, warehouses, and vector stores to enable AI workloads

Hardening proofs-of-concept into production systems: scalability, security, monitoring, and support handover

Data pipelines, lakes/warehouses, and feature stores that power AI (Databricks, Snowflake, Fabric)

Dataset labeling, cleansing, and curation services

Dashboards, natural-language analytics, and AI-assisted reporting (Power BI Copilot, Tableau)

Combining RPA (UiPath, Power Automate, Automation Anywhere) with AI for end-to-end automation

Automated extraction and processing of invoices, forms, contracts, and IDs

AI triage, drafting, and routing of inbound email and customer communications

Sensor data analysis for equipment health and downtime prevention

Anomaly detection, threat intelligence, phishing detection, and SOC automation

Securing AI systems themselves: prompt-injection testing, model access controls, and AI usage policies

Virtual replicas of networks, facilities, or processes for AI-driven what-if analysis

Data scientists, ML engineers, AI/agent developers, prompt engineers, and AI product managers

Workshops and upskilling programs for client teams, from executive briefings to hands-on developer training

Ongoing operation, monitoring, and improvement of client AI solutions under SLA