AI engineering • Cloud • Software
From AI ambition to production reality.
V Solutions engineers production-ready AI agents, enterprise applications and cloud platforms that solve real business problems — from first prototype to secure deployment and ongoing operations.
USA · Canada · IndiaSee how we work
Our engineering
Engineering what comes next.
Four core disciplines. One connected engineering approach. Solutions designed to work in the real world.
Engineers embedded with your team who take a defined business problem from discovery to production software in your environment.
- Problem discovery
- Rapid prototype
- Integration
- Production deployment
- Handover
What we deliver
- Workflow discovery with users and domain experts
- Prototypes tested on real examples
- Integration with enterprise systems
- Deployment, monitoring and handover
Challenges it addresses
- An AI pilot that never reached production
- A process split across systems that don't talk to each other
Typical technology
- Python
- TypeScript
- LLM APIs
- REST & GraphQL
- Kubernetes
Governed agents that complete defined workflows by calling approved tools — with permissions, approvals and audit trails built in.
- Task
- Planning
- Approved tool calls
- Human approval where required
- Result & audit trail
What we deliver
- Single- and multi-agent workflows
- Tool calling and MCP integrations
- Human-in-the-loop approvals
- Evaluation and tracing
Challenges it addresses
- Repetitive multi-step work across several systems
- Assistants that can explain a task but can't do it
Typical technology
- LangGraph
- MCP
- OpenAI
- Anthropic Claude
- OpenTelemetry
Enterprise AI applications that answer from your own knowledge — grounded, permission-aware and measured for quality.
- Enterprise documents
- Retrieval
- Context assembly
- LLM response
- Evaluation & monitoring
What we deliver
- RAG pipelines and knowledge assistants
- Document intelligence
- AI search
- Private and enterprise deployments
Challenges it addresses
- Knowledge locked in documents nobody can find
- AI answers nobody can verify
Typical technology
- Azure OpenAI
- AWS Bedrock
- pgvector
- OpenSearch
- LlamaIndex
Cloud platforms, delivery pipelines and reliability engineering across AWS, Azure and Google Cloud.
- Git commit
- CI pipeline
- Tests & security gates
- Infrastructure & deployment
- Observability
What we deliver
- Architecture and migration
- Terraform, Docker and Kubernetes
- CI/CD and GitOps
- SRE, observability and FinOps
Challenges it addresses
- Releases that need a change window and a specialist
- Cloud spend nobody can explain
Typical technology
- AWS
- Azure
- Google Cloud
- Terraform
- Argo CD
Supporting engineering
Web & Application Development
Custom websites, enterprise web applications, SaaS products and APIs — from architecture and UX through testing, deployment and support.
Virtual Desktop Infrastructure (VDI) Services
Virtual desktop and application delivery on Azure Virtual Desktop, Windows 365 and Citrix, integrated with your identity and endpoint management.
Forward deployed engineering
Your hardest problems. Solved alongside your team.
Forward deployed engineers work directly with your people and domain experts to understand the workflow, integrate the systems you already run, and deliver production software in your environment.
Not staff augmentation. A forward deployed team owns a defined problem end to end — from discovery to a production system your team can run — working with you throughout.
Prototype to production
- Objective
- Understand the real problem and what success looks like.
- Engineering activity
- Working sessions with users and domain experts; map the workflow, the data, the systems involved and the exceptions.
- Deliverable
- A problem statement, a workflow map and agreed success criteria.
- Objective
- Prove or disprove the riskiest assumption quickly.
- Engineering activity
- Build a thin, working slice and test it against real examples from your business.
- Deliverable
- A working prototype and a short findings note with the recommended path.
- Objective
- Connect the solution to the systems it depends on.
- Engineering activity
- Integrate APIs, identity and data sources, following your security model and change process.
- Deliverable
- An integrated build running in a staging environment.
- Objective
- Make it safe and dependable to run.
- Engineering activity
- Automated tests, AI evaluations where relevant, security review, observability and failure handling.
- Deliverable
- Test and evaluation results, dashboards and runbooks.
- Objective
- Release without surprises.
- Engineering activity
- Staged rollout in your environment, with monitoring in place and a rehearsed rollback.
- Deliverable
- A production deployment and its documentation.
- Objective
- Leave your team fully in control.
- Engineering activity
- Pairing, walkthroughs and documentation; optional continued engineering support.
- Deliverable
- Handover sessions and an ownership plan.
Customer-facing engineering
Engineers work with your users and domain experts, not from a specification.
Discovery and prototyping
Short cycles that test the riskiest assumption on real examples first.
AI and application integration
LLM features and agents built into the applications people already use.
Enterprise APIs
Connections to ERP, CRM, ITSM and internal services using your identity model.
Production deployment
Released into your cloud or data center through automated pipelines.
Testing and observability
Automated tests, evaluations, logs, metrics and alerts from the start.
Security and governance
Least-privilege access, data-handling rules and review before go-live.
Knowledge transfer
Documentation, pairing and runbooks so your team owns the result.
- Python
- TypeScript
- LLM APIs
- LangGraph
- REST & GraphQL
- PostgreSQL
- Docker
- Kubernetes
- Terraform
Agentic AI
AI that moves beyond answers.
Governed agents complete defined tasks through approved tools and enterprise integrations. Reliability comes from scoped permissions, evaluation and human oversight — not unlimited autonomy.
Illustrative workflow — an example of how a governed agent system works, not a live system.
A request arrives
A finance user asks to change a supplier's payment terms from 30 to 45 days. The orchestrator breaks the task into steps and decides which agents to involve.
Runs with the requesting user's identity — never broader access.
Checks the policy
Retrieves the approved payment-terms policy and the supplier's contract clause, and cites both.
Reads only from approved, permission-filtered sources.
Queries the system of record
Looks up the supplier's current terms and open invoices in the ERP through a read-only tool.
Read-only tool with a scoped credential.
Prepares the change
Drafts the update and a summary of its effect on open invoices. Nothing is written yet.
Write tools are disabled until approval.
Approves or rejects
A finance approver reviews the proposed change, the sources and the reasoning, then approves or rejects it.
Consequential actions always need a person.
Everything is recorded
The request, each agent's steps, tool calls, sources, the approval and the final result are logged together.
Any outcome can be traced and explained.
- Single-agent and multi-agent systems
- Tool calling
- MCP integrations
- Retrieval-augmented generation
- Enterprise API integrations
- Human-in-the-loop approvals
- Access control
- Evaluation
- Observability
- Deployment and operations
Generative AI
Make your enterprise knowledge work harder.
Where agentic AI acts, generative AI informs: assistants, search and document tools that answer from your own knowledge — with access control, privacy and quality evaluation built in.
Documents and records are collected from approved sources — SharePoint, file shares, wikis, ticketing and databases — along with their access permissions.
Engineering decisions: Connectors, update schedules and which sources are in scope.
Files are parsed (including tables and scanned pages) and split into passages that keep headings and context together.
Engineering decisions: Chunk size and structure tuned to each document type.
Passages are embedded and indexed so a question can find the most relevant ones by meaning, often combined with keyword search.
Engineering decisions: Embedding model, vector store and hybrid search settings.
Retrieved passages are filtered to what the user is allowed to see, re-ranked and assembled into the model's context.
Engineering decisions: Permission filtering, re-ranking and context limits.
The model answers using only the supplied context and the application's instructions.
Engineering decisions: Model choice, prompts, guardrails and cost controls.
The answer cites its sources, or says it doesn't know when the sources don't support one.
Engineering decisions: Citation format and refusal behaviour.
Answers are scored against test questions before release and monitored with user feedback afterwards.
Engineering decisions: Evaluation sets, quality thresholds and dashboards.
What it's used for
- Enterprise knowledge assistants
- Document intelligence
- Internal search
- RAG applications
- AI-powered workflow interfaces
- Customer support assistants
- Data extraction and summarization
Built in, not bolted on
- Retrieval respects existing document permissions
- Private model endpoints where data must stay in your tenant
- Quality measured against real questions before launch
Cloud, DevOps & SRE
Infrastructure built to scale. Delivery built to last.
AWS, Microsoft Azure and Google Cloud — designed, automated and operated as one practice. Service levels are agreed per engagement, never assumed.
- Code
- Build
- Test
- Security
- Deploy
- Observe
Target architectures and migrations planned workload by workload on AWS, Microsoft Azure or Google Cloud.
- Current state
- Target design
- Landing zone
- Migration waves
- Workload assessment and landing zones
- Rehost, replatform or refactor per workload
- High availability and disaster recovery designs
- Rehearsed cutovers with rollback plans
- AWS
- Microsoft Azure
- Google Cloud
Pipelines that build, test, scan and deploy every change the same way — so releases become routine.
- Commit
- Build & test
- Security scan
- Deploy
- CI/CD with automated tests
- GitOps deployments
- Blue-green and canary releases
- Automated rollback on failed health checks
- GitHub Actions
- GitLab CI
- Azure DevOps
- Argo CD
- Flux
Containerized workloads on managed Kubernetes, designed for security, autoscaling and painless upgrades.
- Container image
- Registry
- Cluster
- Autoscaled service
- Cluster design on EKS, AKS or GKE
- Helm charts and GitOps workflows
- Autoscaling and right-sized resources
- Network policies and RBAC
- Docker
- Kubernetes
- Helm
- Karpenter
Reliability treated as engineering: agreed objectives, useful alerts and runbooks people can follow.
- Instrument
- Measure SLOs
- Alert
- Learn
- Service-level objectives and error budgets
- Metrics, logs and traces
- Actionable alerting and on-call runbooks
- Blameless incident reviews
- Prometheus
- Grafana
- OpenTelemetry
- CloudWatch
- Azure Monitor
Security and governance built into the platform and the pipeline rather than reviewed at the end.
- Identity
- Policy checks
- Secrets
- Audit logs
- Least-privilege identity and access
- Managed secrets and encryption
- Policy as code in the pipeline
- Network segmentation and private connectivity
- IAM / Entra ID
- Key Vault / Secrets Manager
- OPA
- Terraform
Cloud spend made visible and owned, then reduced without trading away reliability.
- Tag & allocate
- Analyze
- Right-size
- Review monthly
- Cost allocation by team or product
- Right-sizing and autoscaling
- Commitment and storage-tier planning
- Cleanup of idle resources
- AWS Cost Explorer
- Azure Cost Management
- Kubecost
Architecture
See how intelligence becomes execution.
The layers of a governed AI system — from the people who use it to the platform it runs on. Select a layer to see what it does and how it connects.
- Data flow
- Tool call
- Approval boundary
Users & business interfaces
Where people ask questions, start tasks and review results — inside the tools they already use.
- Web apps
- Portals
- Chat & Teams
- Internal tools
How it connects
- Data flow Sends requests to AI applications and receives answers with sources
- Approval boundary Approvers review proposed actions here before anything changes
AI applications & agents
The application layer: prompts, guardrails and user experience, scoped to a defined job.
- Assistants
- Agents
- AI features in existing apps
How it connects
- Data flow Pulls grounded context from knowledge and retrieval
- Tool call Hands multi-step tasks to the orchestration layer
Agent orchestration
Breaks a task into steps, keeps track of state and decides which agent or tool runs next.
- Planning
- State & memory
- Routing to specialist agents
How it connects
- Tool call Calls only the tools this task is allowed to use
- Approval boundary Pauses before consequential steps until a person approves
MCP servers & approved tools
A governed catalogue of actions — each with a schema, permissions and limits — exposed to agents through one interface.
- MCP servers
- Typed tool definitions
- Argument validation
How it connects
- Tool call Translates validated tool calls into API requests
- Approval boundary Write actions are gated behind the approval boundary
Enterprise APIs & business systems
The systems of record. Agents reach them only through approved tools, with the requesting user's permissions.
- ERP
- CRM
- ITSM
- Internal services
How it connects
- Data flow Returns records to the tool layer for the agent to use
- Approval boundary Changes land only after the approval step has passed
Knowledge sources & retrieval
Indexed enterprise knowledge, filtered to what each user may see, used to ground answers and decisions.
- Documents
- Vector & hybrid search
- Permission filtering
How it connects
- Data flow Supplies permission-filtered passages to applications and agents
Cloud infrastructure & deployment
Where everything runs: infrastructure as code, automated releases and model endpoints in the region your data rules require.
- Kubernetes
- Serverless
- Terraform
- CI/CD
How it connects
- Data flow Hosts every layer above, deployed and versioned through pipelines
Security, governance & observability
Cross-cutting controls that apply to every layer — who can do what, what happened, and whether quality is holding.
- Identity & least privilege
- Audit logs
- Tracing & evaluation
- Cost monitoring
How it connects
- Data flow Collects traces, evaluations and audit events from every layer
- Approval boundary Records every approval and who gave it
Solutions
Built around the problems that matter.
Start from the problem, not the technology. All solutions
AI prototypes that never reach production
- Our approach
- Embed engineers with the team, add the integrations, evaluation, access control and monitoring the prototype is missing, and release it in stages.
- What improves
- An AI feature people rely on every day, with known quality.
Repetitive workflows spread across disconnected systems
- Our approach
- Map the workflow, connect the systems through governed tools, and let agents handle routine steps with approvals where they matter.
- What improves
- Less manual re-keying and faster turnaround on routine work.
Enterprise data that is hard to search and use
- Our approach
- Retrieval over approved sources with permission filtering, answers that cite their sources, and quality measured against real questions.
- What improves
- Answers people can find and verify in seconds rather than hours.
Cloud infrastructure that is expensive or unreliable
- Our approach
- Assess each workload, define it in Terraform, add observability and service-level objectives, and put cost ownership in place.
- What improves
- Predictable spend and fewer surprises in production.
Delivery pipelines that slow down releases
- Our approach
- Automated build, test and security gates with GitOps deployments, progressive rollouts and automatic rollback.
- What improves
- Smaller, more frequent and safer releases.
Legacy applications that limit growth
- Our approach
- Modernize incrementally — new modules and APIs alongside the old system — so the business keeps running throughout.
- What improves
- A codebase that can take on new features and integrations.
How we deliver
From the first conversation to production.
Seven stages, each with something concrete you receive. How we work
- What happens
- Understand the business challenge, the users, the systems involved and what success means.
- You receive
- Problem statement
- Success criteria
- What happens
- Define the solution, architecture, integrations, risks and measurable outcomes.
- You receive
- Architecture diagram
- Scope and acceptance criteria
- What happens
- Validate the highest-risk assumptions with a focused, working solution.
- You receive
- Working prototype
- Findings and recommended path
- What happens
- Build production-quality applications, integrations and infrastructure.
- You receive
- Production code in your repositories
- Infrastructure as code
- What happens
- Test functionality, security, AI quality, performance and operational behaviour.
- You receive
- Test and evaluation results
- Security review
- What happens
- Release through a controlled process with monitoring and a rehearsed rollback.
- You receive
- Production release
- Dashboards and alerts
- Deployment documentation
- What happens
- Documentation, knowledge transfer, operational guidance and — where agreed — continued improvement.
- You receive
- Runbooks
- Handover sessions
- Improvement backlog
Trust & security
Enterprise-ready by design.
Controls built into the architecture and the delivery process — not added at the end.
Least-privilege access
Every service, tool and agent gets only the permissions its job needs.
Secure API integrations
Your identity provider, managed secrets and encrypted connections to every system.
Appropriate data handling
We agree up front which data may go where, including which model providers may see it.
AI evaluation & guardrails
Test sets, automated evaluations and input/output checks before and after release.
Human approval
Consequential actions pause for a person to approve — by design, not by policy alone.
Audit logs & observability
Traces, logs and audit events that make every outcome explainable.
Tested deployments
Releases through pipelines with health checks and rehearsed rollback.
Operational ownership
Documentation and runbooks so your team can run what we build.
How we think
From incident to insight.
Pick a problem to see how our engineers reason from symptoms to a verified fix.
Simulated demonstration — prewritten scenarios for illustration. Nothing here connects to a real system.
Observed symptoms
p95 latency on one API endpoint has roughly tripled. Error rates are flat. It started shortly after a deployment.
Evidence to collect
- Latency by endpoint and release version
- Traces for the slowest requests
- Database query timings and plans
- The diff of the recent deployment
Diagnostic reasoning
Only one endpoint is slow and errors haven't moved, which points to a change in that code path rather than the infrastructure. Traces show the time is spent waiting on one database query that the new release added — and that query isn't using an index.
Remediation
- Roll back or switch off the change behind its feature flag
- Add the missing index through a reviewed migration
- Redeploy the fix
Validation
- p95 back within its objective for a sustained period
- The query plan now uses the index
- A latency regression test and alert are added
Observed symptoms
Pods for one service restart every few minutes. Their last state shows OOMKilled with exit code 137.
Evidence to collect
- kubectl describe pod (last state and exit code)
- Container memory usage against its limit
- Recent changes to limits, traffic or runtime settings
- A heap profile from a running pod
Diagnostic reasoning
OOMKilled means the container exceeded its memory limit. Memory climbs steadily under normal traffic instead of spiking with load, which suggests the runtime heap is sized above the container limit or there is a leak — not a traffic surge.
Remediation
- Align the runtime heap settings with the container limit
- Set requests and limits from observed usage
- Fix the leak if the heap profile confirms one
Validation
- No restarts across a full traffic cycle
- Memory plateaus below the limit
- An alert fires when memory approaches the limit
Observed symptoms
The pipeline fails at the deploy step because the rollout times out. The previous version is still serving traffic.
Evidence to collect
- Pipeline logs for the failed job
- kubectl rollout status and events for the new pods
- Readiness probe results
- The manifest diff for this release
Diagnostic reasoning
The new pods never become ready, and their events show the readiness probe failing. The release moved the health endpoint to a new path, but the probe in the manifest still checks the old one.
Remediation
- Correct the probe path in the manifest (or restore the old endpoint)
- Re-run the pipeline while the previous version keeps serving
- Add manifest validation to the pipeline
Validation
- The rollout completes and probes pass
- Smoke tests pass against the new version
- The validation step catches the same mistake in a test branch
Observed symptoms
An agent's “create ticket” step has started failing. Users see a generic error and the task stops.
Evidence to collect
- Agent traces: prompt, chosen tool and arguments, tool response
- The ticketing API's error logs
- Recent changes to tool schemas, prompts or model version
Diagnostic reasoning
The traces show the model sending a priority value the ticketing API now rejects. The API's allowed values changed, but the tool description given to the model wasn't updated, so the agent keeps generating the old value.
Remediation
- Update the tool definition with the allowed values
- Validate tool arguments before calling the API
- Return a structured error the agent can recover from
Validation
- Replaying the failed traces now succeeds
- Tool-call success rate returns to normal
- These cases are added to the agent's regression suite
Outcomes
Engineering measured by outcomes.
Shorter delivery cycles
Automated pipelines and small, frequent releases replace long change windows.
CI/CD · GitOps
More reliable deployments
Tests, security gates and progressive rollouts catch problems before customers do.
Quality gates · Canary releases
Visible cloud costs
Spend allocated to the teams and products that create it, and reviewed regularly.
Tagging · FinOps reviews
AI that fits real systems
Agents and assistants connected to the tools people already use, with controls in place.
Integration · Governance
Less manual operational work
Routine steps automated, with people kept for judgment and approvals.
Automation · Agents
More maintainable software
Clear architecture, typed code and tests that make change cheaper.
Architecture · Testing
Stronger operational control
Observability, audit trails and runbooks that make systems explainable.
SRE · Audit trails
Engineering proof
Illustrative engineering scenarios
Examples of the problems we take on across our six services — illustrations of the approach, not descriptions of specific client projects. See our case studies
Taking an AI pilot into production
- Challenge
- A support assistant works in demos but isn't connected to ticketing or approved for customer data.
- Approach
- Embedded engineers map the workflow, ground the assistant in approved knowledge, integrate ticketing and add evaluation and monitoring.
IT ticket triage agent
- Challenge
- Routine tickets wait in a queue while engineers gather the same diagnostics every time.
- Approach
- An agent classifies tickets, runs read-only diagnostics, proposes a fix and asks an engineer to approve any change.
Policy assistant for staff
- Challenge
- Employees search several portals for policies and still aren't sure they found the current version.
- Approach
- Retrieval over approved policy documents, answers that cite the exact section, and access that follows existing permissions.
From release weekends to routine deploys
- Challenge
- Monthly releases need a change window, a checklist and the one engineer who knows the steps.
- Approach
- Environments defined in Terraform, automated tests in CI, and GitOps deployments with canary releases and rollback.
Self-service customer portal
- Challenge
- Customers email for order status and documents, and staff re-key answers from three systems.
- Approach
- A portal with single sign-on, connected to the ERP and document store through documented APIs.
Secure access for contractors
- Challenge
- Contractors need a handful of internal applications without being issued company laptops.
- Approach
- An Azure Virtual Desktop host pool publishing only those apps, with conditional access, FSLogix profiles and autoscaling.
Why V Solutions
A partner that stays accountable from first call to production.
Engineering alongside your team
We work with your users and engineers, not from a distance or a specification alone.
Production-focused delivery
Every engagement aims at software that runs in production, not a demo that stops at a slide.
Integration with existing systems
New capabilities connect to the systems you already run, using your identity and access rules.
Security and governance designed in
Permissions, approvals, audit trails and data handling are part of the design from day one.
Measurable acceptance criteria
Success measures are agreed before the build and checked before release.
Knowledge transfer and ownership
Code, infrastructure and documentation live in your accounts, so your team stays in control.
Technology
The ecosystem we engineer with.
Chosen to fit your environment and team. Naming a platform here is not a claim of a vendor partnership.
AI & agents
- LangGraph
- LangChain
- MCP
- OpenAI
- Anthropic Claude
- AWS Bedrock
- Azure OpenAI
- Open-source models
Cloud & platform
- AWS
- Microsoft Azure
- Google Cloud
- Terraform
- Docker
- Kubernetes
- GitOps
- CI/CD
Data & applications
- Python
- TypeScript
- PostgreSQL
- Vector databases
- REST APIs
- GraphQL
- Evaluation tooling
VDI & endpoint
- Citrix
- Azure Virtual Desktop
- Windows 365
- Microsoft Intune
Insights
Practical guidance from our engineers and strategists
AI in VDI and Virtual Workspaces: 2026 Trends for Secure Remote Work with V Solutions Inc.
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Cloud Migration Best Practices 2026: Achieving Zero-Downtime DevOps Transitions with V Solutions Inc.
In 2026, cloud migration best practices have evolved into a critical business imperative, driven by the explosive growth of AI workloads, hybrid/multi-cloud environments, and the…
Citrix Virtual Apps 2026: Bolstering Remote Desktop Security for the Hybrid Workforce
In 2026, Citrix virtual apps 2026 solutions continue to lead the way in secure virtual desktop infrastructure (VDI) and desktop-as-a-service (DaaS), empowering organizations to…
What is Forward Deployed Engineering?
An engineering model in which our developers work directly with your users and teams, close to the problem, to discover what is needed, prototype quickly, integrate with your systems and deploy production software in your environment — then hand it over to your team.
How does V Solutions implement agentic AI?
We start from a defined workflow, give each agent a limited set of approved tools with scoped permissions, require human approval for consequential actions, and log every step. Agents are tested against scenario suites before release and monitored in production.
How are enterprise AI applications evaluated?
With evaluation sets built from real questions and expected answers, scored automatically and by reviewers for accuracy and grounding, plus user feedback and monitoring of quality, latency and cost once the application is live.
Which cloud platforms do you support?
AWS, Microsoft Azure and Google Cloud, including US and Canadian regions. We choose services per workload and define the infrastructure in code so your team can own it.
How does a project engagement work?
It starts with a technical conversation, then moves through discovery, design, a focused prototype, engineering, validation and a controlled deployment, ending with knowledge transfer. Engagements can be fixed-price, time and materials, a dedicated team or outcome-based.
How do you handle security and data privacy?
Least-privilege access, your identity provider, managed secrets, permission-aware retrieval and an agreed policy on which data each model provider may process — with private endpoints or self-hosted models where data must stay in your control.
What happens after deployment?
You receive documentation, runbooks and handover sessions. Where agreed, we continue with monitoring, improvements and support, with scope and response times set in the agreement.
Get in touch
Have a complex problem worth solving?
Let's define the problem, identify the right engineering approach and build a path from idea to production.