Faster task completion
Developers and knowledge workers finish routine drafting, coding and research tasks noticeably faster with AI assistance.
Custom Generative AI
Build custom LLMs, RAG pipelines, AI agents and fine-tuned models around the data and workflows your teams actually use. We take each build from proof of concept through integration, governance and deployment.
Aarvexa AI at a glance
- 0+Years of engineering excellence behind every build.
- 0+Projects delivered across industries.
- 0%Successful go-live rate.
Aarvexa GenAI Studiolive
Policy.pdf §4Audit logLegal wikiHuman review
Guardrails on
Model-agnosticGPT · Claude · Gemini · Llama · Mistral
64 tokens/s
Impact
Independent studies of AI-assisted work report gains across speed, quality and productivity. Your results depend on the use case and how well it is built.
Quicker routine work
Repetitive writing, summarising and analysis steps move from hours to minutes when AI handles the first pass.
Higher-rated output quality
Human-reviewed AI output is often rated higher on structure, clarity and completeness than unaided first drafts.
Stronger support productivity
Support teams resolve more requests per hour when AI suggests answers grounded in approved knowledge.
Note: Results vary by task, user expertise, model quality, workflow complexity, data readiness and implementation. Bars are illustrative, not measured outcomes.
Business Data
Connect generative AI with approved documents, databases and knowledge sources for more relevant and reliable outputs.
Custom Logic
Apply business rules, prompts, workflows and controls so AI behaves according to defined requirements.
Human Oversight
Keep important outputs, actions and decisions subject to human review and approval where needed.
The Basics
Generative AI development turns foundation models into useful business software. It connects AI models with your data, APIs, rules and workflows, so teams can generate content, answer questions, analyse information and automate repeatable work with the controls the use case requires.
Services
Aarvexa provides generative AI software development services spanning strategy, custom builds and production deployment. Each service takes a project from proof of concept to a system your team actually uses.
Generative AI Consulting
Scopes use cases, assesses data readiness, and selects the right models and architecture before committing engineering resources to a build.
Custom LLM Development
Trains and fine-tunes large language models on your data and terminology, then wraps them with the API and interface layer your product needs.
Custom RAG Development
Grounds model responses in your own documents and databases through retrieval pipelines, reducing hallucination and keeping answers traceable to a source.
AI Agent and Chatbot Development
Builds conversational assistants and task-executing agents, including multi-agent orchestration, that connect to real systems and escalate to humans when needed.
Model Fine-Tuning and Optimization
Adapts foundation models to domain terminology and response requirements, then tunes for latency, cost and output quality against defined benchmarks.
Generative AI Evaluation and Testing
Benchmarks accuracy, checks for hallucinations and red-teams edge cases against defined criteria before a model reaches production traffic.
Integration and Deployment
Connects generative AI to CRMs, ERPs and internal APIs, then deploys through staged releases with the monitoring production traffic requires.
Governance and Risk Management
Builds access controls, audit logging and human review into the architecture from the start, instead of adding oversight after deployment.
GenAI Support and Optimization
Maintains and improves deployed generative AI systems by refining prompts, updating retrieval sources and adjusting workflows as business needs change.
The Problem
POCs that stall before deployment
Proofs of concept often demonstrate technical feasibility but fail to address scalability, integration, ownership, security and production readiness.
Hallucination and factual accuracy risks
Generative AI can produce confident but incorrect outputs when systems lack grounding, validation, retrieval controls and evaluation frameworks.
Weak integration with existing systems
GenAI tools create limited value when disconnected from CRMs, ERPs, databases, APIs and the workflows where teams actually perform daily work.
Compliance and data governance gaps
Projects slow down when data access, privacy, auditability, retention and permissions are not designed into the architecture early.
Tech Stack
We choose models and frameworks around accuracy, privacy, hosting, latency, scalability and cost, instead of defaulting to whichever technology is getting the most attention.
Foundation Models
- OpenAI GPT models
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
- DeepSeek
Agent and LLM Frameworks
- LangChain
- LangGraph
- CrewAI
- AutoGen
Vector Databases
- Pinecone
- Weaviate
- Qdrant
- pgvector
Deployment Infrastructure
- Docker
- Kubernetes
- vLLM
- AWS
- Microsoft Azure
- Google Cloud
Compare
Custom generative AI gives you greater control over data, integration, accuracy, governance, scalability and product differentiation than generic off-the-shelf tools.
RequirementCustom Generative AIOff-the-Shelf AI
Domain accuracyGrounded in your data, terminology and workflowsDesigned for broad general-purpose usage
Data privacyDeployment and access controls defined around your environmentData handling depends on vendor policies
Integration depthConnects directly with APIs, databases, CRMs, ERPs and internal systemsUsually limited to supported integrations
Workflow controlLogic, guardrails, approvals and outputs are configurableWorkflow behaviour follows product limitations
Cost at scaleArchitecture can optimise model routing and inference usagePricing remains dependent on vendor plans
Product differentiationCreates proprietary AI capabilities around your productCompetitors can access similar functionality
GovernanceMonitoring and control mechanisms fit your requirementsGovernance options depend on vendor features
Process
Our process starts with a business problem worth solving and moves through architecture, validation, integration and deployment, with clear checkpoints for quality, risk and ROI.
Discovery and readiness assessment
We define the business problem, users, workflow, data readiness, risk exposure and success metrics before committing engineering resources to the build.
Architecture and data design
We design the data, retrieval, model, orchestration, security and integration layers around the reliability and scale the production workflow needs.
Proof of concept build
A focused proof of concept validates feasibility, output quality, user value, integration assumptions and measurable business impact.
Model development and fine-tuning
Selected models get configured, fine-tuned, evaluated and optimised against your data, domain terminology, response requirements and latency targets.
Integration and deployment
The solution integrates with business systems, security controls get implemented, production workflows are tested and release happens in controlled stages.
Monitoring and continuous optimization
Output quality, usage, cost, retrieval performance and workflow outcomes are monitored continuously to improve production performance over time.
Engagement
Choose the delivery model that fits where your project stands now, how much support your team needs and how quickly you want to move toward production.
POC Sprint
Validate one clearly defined GenAI use case through architecture, prototyping, evaluation and feasibility testing before committing to larger production development.
Option 1MVP Build
Build a production-oriented GenAI application with core integrations, model controls, user workflows, security, testing and deployment infrastructure included.
Option 2Full-scale Rollout
Expand validated AI capabilities across products, teams, workflows and infrastructure while adding governance, monitoring, optimisation and production support.
Option 3Staff Augmentation
Add experienced AI, ML, LLM, data and MLOps engineers directly to your team without expanding permanent internal hiring commitments.
Option 4Business Impact
Generative AI creates value when it reduces manual work, accelerates access to knowledge, improves decisions and strengthens high-volume business workflows.
Faster access to knowledge
Grounded retrieval turns scattered documents and data into searchable, contextual answers, cutting the time employees spend hunting for information across disconnected systems.
Automated multi-step work
AI agents complete document-heavy, multi-step tasks end to end, following defined workflows with human review at the points that matter most.
Lower cost at scale
Model routing, retrieval optimisation and usage monitoring keep inference spending aligned with actual workload instead of scaling linearly with usage.
Reduced integration overhead
Modular APIs and observable services connect generative AI to existing CRMs, ERPs and internal tools without rebuilding the systems already in place.
Lower risk in production
Access controls, evaluation frameworks and human review get built into the architecture from day one, reducing the chance of costly failures after launch.
Continuous performance gains
Output quality, retrieval accuracy and cost get monitored after deployment, so the system improves with real usage instead of degrading over time.
Risk
Production GenAI introduces accuracy, privacy, bias, compliance, cost and integration risks that must be controlled through architecture, testing and monitoring.
RiskHow We Manage It
Hallucination and inaccurate outputsGround models with validated RAG sources, retrieval controls, structured outputs, verification rules and human review where required.
Data privacy exposureUse isolated environments, encrypted data flows, access controls, PII filtering, private hosting and restricted model access.
Bias in generated contentTest representative inputs, establish output criteria, implement guardrails and continuously evaluate model behaviour.
Regulatory non-complianceAdd traceable model interactions, policy controls, audit logs, data handling rules and documented review processes.
Cost overruns at scaleMonitor token consumption, route workloads across models, optimise retrieval, cache repeated requests and measure per-workflow inference cost.
Integration failureUse modular APIs, observable services, controlled system permissions, fallback logic and staged production releases.
Governance
Governance is built into the system architecture using access controls, evaluation, audit logging, human review and documented model lifecycle processes.
Bias and fairness testing
Model outputs are evaluated across representative inputs, edge cases and defined quality criteria to identify inconsistent or potentially biased behaviour.
Data privacy by design
Access permissions, encryption, filtering, environment isolation and configurable data retention policies control what data reaches each model.
Human-in-the-loop review
Human approval is added where model outputs affect sensitive, regulated, financial, clinical or otherwise high-consequence business decisions.
Audit-ready documentation
Models, prompts, datasets, evaluation criteria, system changes and deployment decisions get documented to support internal governance processes.
Industries
As a generative AI development company, Aarvexa adapts each build to industry-specific workflows, data structures, risk requirements and integration needs instead of applying generic templates.
Healthcare
Healthcare GenAI solutions cover documentation, knowledge retrieval, summarisation and administrative workflows with controlled data access and human review.
Finance
Governed GenAI workflows support research, reporting, customer operations, risk analysis and document processing with traceable outputs and access controls.
Insurance
Governed generative AI supports claims summarisation, underwriting narratives and policy documentation, with traceable outputs suited to regulated customer communications.
Sports
Generative AI supports performance reporting, scouting summaries and fan-facing content generation from sports data and video.
Retail
Generative AI automates product content, improves search, supports recommendations and creates more relevant customer interactions across digital channels.
Agriculture / AgTech
Generative AI applies to reporting, operational knowledge, field intelligence, livestock insights and decision support across complex agricultural workflows.
SaaS
Software companies embed copilots, assistants, intelligent search, content generation and workflow automation directly into their existing digital products.
Transportation and Logistics
GenAI applications support shipment documentation, dispatch summaries and operational reporting across complex, high-volume transportation and logistics workflows.
Hospitality
Generative AI drafts guest communications, personalises recommendations and summarises reviews and feedback across properties and booking channels.
Compare
Traditional AI predicts, generative AI creates, and agentic AI executes. Production systems often combine all three depending on workflow complexity and business requirements.
CapabilityTraditional AI/MLGenerative AIAgentic AI
Primary rolePredicts, classifies and identifies patternsCreates new content and structured outputsPlans, decides and executes multi-step tasks
How it worksUses trained statistical or predictive modelsUses foundation models to generate responses from learned patternsUses models with tools, memory, workflows and decision logic
Best forPrediction, scoring, classification and detectionGeneration, summarisation, search and assistanceWorkflow automation, orchestration and task execution
Level of autonomyLowModerateHigh with defined controls
Tool usageUsually limitedCan call tools when integratedDesigned to use APIs, applications, databases and external tools
Memory and contextUsually task-specificUses prompt and retrieval contextCan maintain context and memory across multiple workflow steps
Human oversightUsed for model validation and decisionsUsed for sensitive or high-impact outputsBuilt into approval, escalation and exception workflows
Typical examplesFraud detection, forecasting, churn prediction, recommendation scoring, computer visionDocument summarisation, content generation, copilots, knowledge assistants, conversational searchClaims processing, research agents, support automation, approval workflows, task orchestration
Case Studies
Sample engagements showing how custom generative AI is applied. Details are illustrative.
Generative AI Content Moderation
AI Content Moderation ImpactSample view
AI content moderationAutomated
User engagementIncreased
Supported user baseGrowing
Generative AI Content Moderation Case Study · Sample
Safe social networking with generative AI and ML
A social platform needed to grow engagement while controlling inappropriate content and supporting age-specific safety requirements. Aarvexa AI builds AI-powered moderation, a conversational assistant, personalised interactions and reporting workflows across the platform.
- AI-basedContent moderation
- HigherUser engagement
- ScalableSupported user base
- AI-based moderation using managed content-safety services, with human intervention available for flagged content.
- LLM chatbot supporting intent classification, response generation, file-based queries and document vectorisation.
- Real-time moderation and reporting workflows across user profiles, messages and shared platform content.
- Personalised experiences, quizzes, games and value-based features designed to increase interaction.
Why Aarvexa
As a generative AI development company, Aarvexa helps teams move beyond demos into systems that connect with real workflows, operate with clear controls and can be measured in production.
Engineering first
Work starts with architecture, data, integrations, evaluation, deployment requirements and measurable outcomes instead of presentations about generic AI possibilities.
Production focus
Design decisions centre on real users, production traffic, security, latency, failure handling, observability and ongoing ownership from the beginning.
Cross-industry experience
AI teams have delivered solutions across healthcare, finance, agriculture, retail, software, insurance, hospitality and other operationally complex industries.
Governance by design
Security, access control, output evaluation, human review, monitoring and traceability are included in the architecture instead of being added after deployment.
Flexible engagement
Start with readiness, validate through a focused POC, build an MVP, scale an existing solution, or extend your internal engineering team.
Senior AI expertise
Work directly with experienced AI engineers who deliver generative AI solutions across models, data engineering, integrations, MLOps, software architecture and production deployment.
More Solutions
Extend your generative AI capabilities with connected services for enterprise integration, knowledge retrieval, model customisation and prompt optimisation.
RAG Development
Ground model answers in your documents and databases with retrieval pipelines.
Generative AI Integration
Connect GenAI to CRMs, ERPs, APIs and internal tools.
LLM Fine-Tuning
Adapt foundation models to your terminology, tone and tasks.
Custom Model Development
Train and optimise models built around your data and use case.
NLP Development
Extract entities, intent and meaning from text and speech.
Prompt Engineering
Design, test and version prompts that produce reliable outputs.
Procurement Repository
Search and summarise vendor, contract and purchasing documents.
Workflow Automation
Automate multi-step business processes with AI and human review.
RFP Responses
Draft accurate, consistent RFP answers from approved content.
Deployment Services
Release and operate GenAI systems on cloud, edge or on-premises.
FAQ
Straight answers on generative AI models, accuracy, integration, governance, pricing and who we are.
They cover designing, building and deploying AI systems that generate text, answers, summaries, code or other content, such as custom LLMs, RAG pipelines, AI agents and fine-tuned models, connected to your data and workflows.
We begin with a discovery and readiness assessment: the business problem, users, workflow, data readiness, risks and success metrics. That points to the highest-value, most feasible use case first.
Not always. RAG and prompt-based solutions can start from your existing documents and knowledge sources. Fine-tuning needs more structured examples, which we assess in the data readiness step.
A focused POC sprint is typically the quickest route to validate one use case. Timelines depend on scope, data readiness and integrations, and we agree them after discovery.
Healthcare, finance, insurance, sports, retail, agriculture, SaaS, transportation and logistics, hospitality and more.