01
Define the outcome
Map the business workflow, data sources, tools, permissions, and actions the AI system needs.
AI Agent & Intelligent Application Engineering
We build software that thinks, acts, and automates.
DeepVention builds production-grade AI agents and intelligent applications. We turn business workflows into secure, scalable agentic systems that can reason, use tools, automate work, and take action. We also build AI-native web and mobile products designed for real users, real data, and real-world scale.
01 — AI Agents
AI Agent Development
We build task-oriented agents that can reason, call tools, work with APIs, maintain controlled context, and complete multi-step workflows.
This includes internal copilots, operational agents, research agents, support agents, sales and RevOps agents, and custom domain-specific agents.
- Tool/function calling and custom MCP servers
- Controlled context, retries, and failure handling
- Multi-step workflows with human approval where needed
02 — Agentic Applications
Agentic Applications
We build complete AI-native web applications where agents are part of the product architecture rather than a chatbot added on top.
We design the application, data layer, agent orchestration, permissions, human approvals, failure handling, and user experience as one production system.
- Product interfaces designed around agent actions
- State, permissions, and approval boundaries
- Web applications with agents at the core
03 — Workflow Automation
Workflow Automation and Integrations
We connect agents to the software your business already uses through APIs, webhooks, MCP, databases, and custom integrations.
Agents can retrieve information, update systems, trigger actions, prepare outputs, and route high-risk decisions to humans.
- API, webhook, database, and event-driven workflows
- CRM, ERP, and internal-system integration
- Tool access with explicit approval paths
04 — Context & Knowledge
RAG, Context, and Knowledge Systems
We build retrieval pipelines over company documents and structured data so agents receive the right information at the right time.
This includes vector search, context assembly, grounding, citations, permissions, memory patterns, and evaluation.
- Document and structured-data retrieval
- Grounding, citations, and controlled memory
- Permission-aware context assembly
05 — AI-Native Products
AI-Native Product and MVP Development
We turn new product ideas into working AI applications quickly using modern AI-assisted engineering.
Senior review, clean architecture, testing, observability, security, and production standards stay in the loop from day one.
- AI-native web product architecture
- MVP delivery with production boundaries
- Testing, observability, and maintainable foundations
06 — Trust & Operations
AI Security, Guardrails, Evals, and Observability
We make agent behavior reviewable, testable, observable, and constrained by the risk of the work it performs.
We implement controls around inputs, outputs, data exposure, tool authorization, human approval, policy enforcement, traces, evaluations, and release decisions.
- Prompt-injection and untrusted-content defenses
- Evaluation datasets, traces, and release gates
- Least privilege, auditability, and human oversight
07 — Product Engineering
Full-Stack MERN Engineering
We build and extend production applications using MongoDB, Express.js, React, Node.js, Next.js, TypeScript, PostgreSQL, Supabase, AWS, and related technologies.
AI is only useful when the surrounding application, APIs, data model, permissions, and infrastructure are reliable.
- React, Node.js, MongoDB, Express.js, Next.js, and TypeScript
- PostgreSQL, Supabase, AWS, Firebase, and Docker
- Reliable APIs, data models, and product interfaces
08 — Production Readiness
Production Hardening, Scale, and Mobile
If an existing prototype was built with Lovable, Bolt, v0, Replit, Cursor, or another AI coding tool, we can audit and refactor it for production.
We fix architecture, authentication, security, testing, error handling, database performance, background processing, deployment, and observability while keeping AI agents and intelligent applications as the primary positioning.
- Production audits and code refactoring
- Scalable deployment and operational reliability
- Cross-platform iOS and Android experiences connected to the same AI systems
How we work
From business outcome to an AI system your team can operate.
02
Design the architecture
Choose the product surface, agent boundaries, integrations, context strategy, and human controls.
03
Build the product
Engineer the application, data layer, agent runtime, APIs, workflows, and user experience as one system.
04
Test and operate
Evaluate behavior and failure cases, add observability, and give your team a system it can operate and extend.
FAQ
Questions technical teams ask before production.
When should we use one agent instead of multiple agents?
Use one agent when a single reasoning loop can own the workflow cleanly. Multi-agent designs are justified when specialization, parallelism, separate permissions, or independently testable responsibilities outweigh the additional state and coordination complexity.
Can you work inside our existing AWS, Azure, or GCP environment?
Yes. The architecture should fit the customer's existing identity, networking, secrets, deployment, and observability standards rather than forcing a separate AI-only platform.
Can you integrate agents into an existing SaaS product?
Yes. Production integration is a core service: application APIs, data, identity, permissions, tenant boundaries, tool access, evaluation, observability, and operator workflows are treated as part of the system.
How do you evaluate agent reliability?
We define representative datasets and failure cases, capture traces and outputs, score the behaviors that matter to the workflow, and use regression checks before release. The exact evaluation strategy depends on the product risk and task.
Can you build custom MCP servers?
Yes. We build MCP servers and gateway layers with explicit identity, authorization, tool boundaries, rate limits, audit logs, and deployment controls when MCP is the right integration interface.
How do you handle sensitive data?
We design around least privilege, scoped tool access, sensitive-data handling, redaction when the approved sensitive-data policy requires it, controlled logging, tenant isolation, and human approval for high-impact actions. Legal compliance remains the customer's responsibility and depends on the complete system and operating environment.
Do you provide ongoing AgentOps?
Yes. Ongoing work can include evaluation maintenance, telemetry review, failure analysis, release regression, incident learning, and reliability improvements.
Do you guarantee compliance?
No. DeepVention implements technical controls that can support governance and compliance objectives, but certification and legal compliance depend on the customer's full organization, policies, infrastructure, and operating practices.