Solvren AI

Production AI, built inside your own cloud in 6 to 8 weeks.

Solvren AI is an AI forward deployment company for mid-market regulated teams. Our engineers join your team, ship one workflow in your account on the cloud and models you already approved, and hand you the code, the infrastructure and the runbooks. We are vendor-neutral, so nothing in the build depends on a platform we sell.

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An 8-week deployment plan
  1. Scope and access Weeks 1 to 2 One workflow, agreed metrics, access under your security rules.
  2. Build in your cloud Weeks 3 to 5 Weekly demos, evaluated against the agreed metrics.
  3. Harden and hand over Weeks 6 to 8 Monitoring, runbooks, and your team owns the code.

Week 6: security review with your team, before anything reaches production

Weeks 1 to 8: your data stays in your account

Drawing
One production AI workflow
Client environment
Your AWS, Azure or GCP account
Sectors
Biotech, healthcare, defense, legal and fintech
Partner status
OpenAI Select Partner, AWS Partner Network member
Prepared by
Solvren AI LLC, San Diego, California
Founded
2026

What is forward deployment?

Forward deployment means our engineers embed with your team and build the AI system inside your own cloud account, instead of selling you a platform or handing over a slide deck. We scope one production workflow, ship it in 6 to 8 weeks, and leave your team owning the code, the infrastructure, and the runbooks. Because the system runs in your AWS, Azure, or GCP environment, your data never passes through our infrastructure or an unapproved third-party API. That is why forward deployment fits regulated buyers in biotech, healthcare, defense, legal, and fintech: security review happens against infrastructure you already control, and every control we add (scanning gates, spending limits, audit trails) is visible to your own auditors.

How we deploy, step by step

What we deploy

Three systems, each built inside the client’s cloud. Each page covers the problem, what we deployed, how it works, the controls, and only the results we measured.

All case studies

  • Biotech, Healthcare, Defense

    AI Skills Registry with Security Scanning

    A private, scanned catalog of agent skills that runs inside the client’s cloud, so no skill reaches an agent without passing a security gate.

    1. Author submits skill
    2. Scanner runs
    3. Triage policy decides
    4. Registry publishes
    5. Agent installs

  • Fintech

    Agentic Payments on Amazon Bedrock AgentCore

    Agents that can pay for approved APIs and data within hard limits a human sets in advance, with a receipt and audit trail for every payment.

    1. Agent
    2. Application
    3. AgentCore Payments
    4. Paid API

  • Legal, Healthcare

    Class 1 Decider: A Fast Router for LLM Requests

    A small, very fast decision layer in front of LLM calls that sends each request to the right model, tool, or agent without paying a large model to decide.

    1. Request arrives
    2. Class 1 Decider
    3. Route
    4. Fallback

What stays with you

Every system we build runs in your cloud, belongs to you, and outlasts our engagement. No vendor lock-in and no recurring platform fees.

Code
Lives in your repositories from the first commit.
Infrastructure
Runs in your AWS, Azure or GCP account, under your own access policies.
Data
Never passes through our infrastructure or an unapproved API.
Runbooks
Written for your team to operate the system after we leave.

From kickoff to production in 6 to 8 weeks

  1. Scope and access

    Weeks 1 to 2

    We pick one production workflow, agree success metrics, and get access to your cloud account and data under your security team’s rules.

  2. Build in your cloud

    Weeks 3 to 5

    Embedded engineers build the system inside your AWS, Azure, or GCP account, with weekly demos and evaluation against the agreed metrics.

  3. Harden and hand over

    Weeks 6 to 8

    Security review with your team, monitoring, and runbooks. The system goes to production and your team owns the code and infrastructure.

  4. Optimize and scale

    Ongoing

    A monthly retainer covers monitoring, improvements, and new features, so the system keeps improving instead of going stale.

Why AI projects stall

Four failure modes we hear from companies that tried AI before finding us, and what we do differently.

  • The pilot graveyard

    Your team built a ChatGPT wrapper. Everyone was impressed in the demo. Six months later it's still a demo: never deployed, never measured, never trusted. Management has quietly written off AI as hype.

    Our answer: We don't build pilots. Every engagement ends with a system running in your environment.

    Read the case studies

  • Strategy without engineers

    A consulting firm billed $300K to produce an AI roadmap. The deck was thorough. Then they handed it to your team and left. No engineers. No implementation. No working system. Just a PowerPoint.

    Our answer: We're engineers who've shipped AI in production, not consultants who advise from the sideline.

    About Solvren AI

  • Compliance killed every vendor

    Your security team reviewed every AI vendor pitch. Every one failed: HIPAA, CMMC, ITAR, SOC 2. Sending your data to an external API is a non-starter. So every POC died in the security review.

    Our answer: We deploy in your cloud: AWS, Azure, or GCP. Your data never touches our infrastructure.

    RAG pipelines in your cloud

  • The vendor that disappeared

    You hired an AI vendor. They shipped version one, collected payment, and stopped answering Slack. The model degraded. No one monitored it. Your team inherited something they can't maintain.

    Our answer: We offer monthly retainers for monitoring, optimization, and iteration. We stay after launch.

    Ongoing support and other services

AI that ships to production

Every engagement ends with a system running in your environment, not a pilot or a prototype. Not sure where to start? Begin with the audit. Pricing is on request.

  • AI readiness audit Start here

    We map your workflows, identify your top 3 AI opportunities, and deliver a clear ROI roadmap in 2 weeks.

  • AI agents and chatbots

    Custom AI agents that handle customer support, sales qualification, internal helpdesks, and complex multi-step workflows.

  • RAG pipeline development

    Retrieval-augmented generation systems that let your team query internal documents, contracts, and data with natural language.

  • Custom LLM development

    Fine-tuned language models trained on your domain data, from dataset preparation to deployment on AWS Bedrock or Azure.

  • Workflow automation

    AI-powered automation that eliminates repetitive tasks: document processing, data extraction, reporting, and more.

  • Ongoing AI support

    Monthly retainers for model monitoring, prompt optimization, performance tuning, and adding new capabilities.

Built for regulated, complex industries

We work to the compliance, security and precision requirements of high-stakes industries.

All industries

  • Biotech and life sciences

    Clinical data extraction, drug discovery AI, regulatory document automation, and lab workflow optimization.

  • Healthcare

    AI designed for HIPAA requirements: patient intake, clinical note summarization, prior auth automation, and care coordination.

  • Defense and aerospace

    Secure, on-premise AI for document intelligence, threat analysis, and mission planning support systems.

  • Legal

    Contract review AI, legal research automation, due diligence pipelines, and matter management systems.

  • Fintech

    Fraud detection, risk modeling, document processing, and AI-powered customer service for financial services.

Partner status with the platforms we deploy on

We hold partner status with the model and cloud providers our clients run in production, which means direct escalation paths, roadmap visibility, and no reseller in between. We still build on whichever cloud and models your team has approved.

What our partnerships mean for your build

We have shipped AI at scale. Now we do it for you.

Most AI firms are consultants who have never deployed a model in production. We are engineers who have, and we built the company around that difference.

About Solvren AI
Built at enterprise scale
Our team has built AI systems for large-scale production operations. We know what production AI actually looks like, not just in demos.
Production, not prototypes
You see weekly demos along the way, but every project ends with a deployed system in your environment. Not a Jupyter notebook. A system your team actually uses.
Fixed scope, fixed timeline
Each engagement covers one production workflow with a stated scope and a 6 to 8 week timeline, so you know what ships and when.
Your data stays yours
We build on your cloud infrastructure: AWS, GCP, or Azure. Your data never leaves your environment, and security review happens against infrastructure you already control.
Forward-deployed in San Diego
Our engineers embed with your team, on-site when it helps, not from a ticket queue. We understand the biotech, defense, and tech ecosystem here, and we show up when things need fixing.
We stay after launch
Most vendors disappear after delivery. We offer ongoing retainers to keep your AI optimized, monitored, and evolving with your business.

Common questions

See all questions
What does forward deployment mean?

Forward deployment means engineers embed with your team and build the AI system inside your own cloud account, rather than selling a platform or a strategy deck. Solvren AI is an AI forward deployment company: we scope one production workflow, ship it in 6 to 8 weeks, and hand your team the code, infrastructure, and runbooks.

How do you deploy an LLM without sending data to external APIs?

Run the model and the retrieval stack inside your own cloud account, using a managed service your security team has already approved (such as Amazon Bedrock or Azure OpenAI in your tenancy) or an open-weight model on your own compute. Keep logs, vector stores, and prompts in the same account, and block outbound calls the system does not need. That is the default setup for every Solvren deployment.

How long does a secure RAG deployment take?

A scoped RAG system over one document set typically reaches production in 6 to 8 weeks: about two weeks for discovery and data access, three to four for building retrieval and evaluation, and the rest for security review and handover. The biggest variable is how quickly data access and security approvals happen, not the model work.

What cloud platforms do you work with?

We build on AWS (Bedrock, SageMaker), Azure (OpenAI Service), and GCP (Vertex AI). Your data and systems stay in your cloud environment.

How do you handle data privacy and compliance?

All systems are built in your cloud environment, and your data never touches our infrastructure. We design to the requirements your auditors already use, such as HIPAA, SOC 2, and ITAR. Certification stays with your organization and its auditors; we give them the architecture, controls, and logs they need to review.

Start with one workflow

Book a free 30-minute AI audit. We look at one workflow with you and tell you whether it can reach production in 6 to 8 weeks. No commitment, just 30 minutes with an engineer.

Book a free AI audit
Book a free AI audit