AI Infrastructure
We build the AI infrastructure. You focus on the business.
Off-the-shelf AI tools solve generic problems. Your business has specific ones. We design, build, and deploy custom AI solutions on your infrastructure: RAG systems, automated analysis, intelligent workflows, and more. Everything self-hosted, privacy-first, no vendor lock-in. You own the code, the data, and the solution.
Your infra
Every solution runs on your cloud or on-premises. Data never leaves your environment.
Your code
Full source code handoff. No proprietary wrappers, no vendor lock-in. Fork it, modify it, own it.
PoC first
We validate every solution with a proof of concept before committing to full build. No wasted investment.
Production
Not a demo. IaC, CI/CD, monitoring, alerting, documentation. Production-grade from day one.
Why custom AI
ChatGPT can answer questions. But can it analyze your test logs?
Generic AI tools are powerful but blind to your data, your processes, and your domain. Custom AI solutions connect directly to your systems and solve specific business problems.
Solves your specific problem
Not a general chatbot. A system designed around your data, your workflows, and your business logic. Trained on your documents, connected to your tools, tailored to your team.
Data stays with you
Sensitive documents, customer data, source code, test results: nothing leaves your infrastructure. Self-hosted models when needed. External APIs only through your controlled gateway.
Measurable ROI
Every project starts with a clear business problem and success criteria. We track impact from PoC through production. If the PoC doesn't prove value, we don't build further.
What we build
Real solutions for real business problems
These are examples of solutions we've designed and deployed. Each one started with a specific pain point and ended with a production system that runs 24/7 on the client's infrastructure.
RAG on Internal Documents
The problem
Employees waste 30+ minutes searching Confluence, SharePoint, and shared drives for answers that exist somewhere in thousands of documents.
What we build
Retrieval-augmented generation system that indexes your entire knowledge base. Employees ask questions in natural language, get answers with source references. Self-hosted, respects access permissions, GDPR-compliant.
⚡Answers in 30 seconds instead of 30 minutes. Knowledge stays inside the company. New hires onboard faster.
AI for HR & Recruitment
The problem
HR teams manually review hundreds of CVs per position. Bias is hard to control. Screening takes days or weeks.
What we build
AI-powered candidate matching and scoring system. Analyzes CVs against job requirements, generates structured evaluations, suggests interview questions tailored to each candidate's profile. All data stays internal.
⚡Time-to-shortlist reduced by 70%. Structured scoring reduces unconscious bias. No candidate data leaves your systems.
Test Log Analysis
The problem
QA teams spend hours analyzing regression test failures across 1000+ tests. Root causes are buried in log files. Same failures keep recurring.
What we build
Automated analysis engine that ingests test results, correlates failures across test suites, identifies patterns, and suggests root causes based on historical data. Integrates with your CI/CD pipeline.
⚡Root cause identification in minutes instead of hours. Recurring failures flagged automatically. QA team focuses on fixing, not digging.
AI-Powered CI/CD
The problem
Deployment pipelines break unpredictably. Code reviews take too long. Teams don't know whether a release is risky until it's in production.
What we build
Intelligent pipeline analysis that scores deployment risk based on code changes, historical failure patterns, and test coverage. Automated code review suggestions. Smart test selection to run only what matters.
⚡Deployment risk scoring before every release. 40% fewer pipeline failures. Code review time cut in half.
Compliance Documentation Automation
The problem
Maintaining SOC 2, ISO 27001, or NIS2 documentation is tedious. Documents go stale within weeks. Auditors find gaps every time.
What we build
AI system that reads your Infrastructure as Code, security configurations, and policies, then generates and maintains compliance documentation automatically. Detects drift between documentation and reality.
⚡Compliance docs always up to date. Audit prep time reduced by 80%. Drift detected within hours, not months.
Your Custom Use Case
The problem
You have a specific business challenge that off-the-shelf tools don't solve. Your data is unique, your processes are specific, your requirements go beyond what generic AI can handle.
What we build
We start with a 2-week needs assessment: understand your data, map your workflows, define success criteria. Then build a proof of concept to validate the approach before committing to a full build.
⚡A solution built for your exact problem, on your infrastructure, with your data. No compromises.
Our process
From business problem to production system in 6 steps
Every project follows the same proven process. We validate before we build, and we hand off everything when we're done.
Step 1
Needs Assessment
We understand your problem before we propose a solution.
- → Business problem definition and success criteria
- → Data audit: what data exists, where, in what format
- → Workflow mapping: how does the team work today
- → Feasibility assessment: can AI solve this, and is it worth it
- → Go/no-go recommendation (we'll be honest if AI isn't the answer)
Step 2
Architecture & PoC
We design the solution and prove it works before you commit.
- → Solution architecture document
- → Model selection: open-source vs. API, hosted vs. self-hosted
- → Technology stack recommendation
- → Working proof of concept on a subset of your data
- → PoC evaluation against defined success criteria
Step 3
Build & Deploy
Full production implementation on your infrastructure.
- → Application code: APIs, pipelines, integrations
- → Infrastructure as Code (Terraform, Helm)
- → CI/CD pipeline for the AI system itself
- → Monitoring, alerting, and observability
- → Security review and hardening
Step 4
Integration
We connect the solution to your existing tools and workflows.
- → Data source connections (databases, APIs, file systems)
- → SSO and access control integration
- → CI/CD pipeline integration (for DevOps use cases)
- → Communication tools (Slack, Teams, email)
- → Existing dashboards and reporting tools
Step 5
Handoff & Training
Your team owns the solution. We make sure they're ready.
- → Complete documentation: architecture, operations, troubleshooting
- → Code walkthrough sessions with your engineering team
- → User training for business teams
- → Runbook for common operations and incident response
- → Knowledge transfer sign-off
Step 6
Managed Support (optional)
Ongoing maintenance, monitoring, and optimization.
- → Model performance monitoring and retraining
- → Infrastructure updates and security patches
- → Cost optimization reviews
- → Feature iterations based on user feedback
- → Available as part of DevOps as a Service package
Technologies we work with
We pick the right tool for the job, not the other way around
No religious attachment to any framework. We choose based on your requirements, your infrastructure, and what delivers results fastest.
LLM providers
OpenAI, Anthropic, Azure OpenAI, Mistral, Cohere. For self-hosted: Llama, Qwen, DeepSeek via vLLM or Ollama. We benchmark models on your actual data before choosing.
RAG frameworks
LlamaIndex, LangChain, Haystack. Vector databases: Qdrant, Weaviate, pgvector. Embedding models selected per use case and language requirements.
Orchestration
LangGraph, CrewAI, custom pipelines. For complex workflows with multiple steps, tool use, and human-in-the-loop. Agent frameworks when autonomy is needed.
Observability
Langfuse for LLM tracing. Grafana and Prometheus for infrastructure. OpenLLMetry for standardized AI metrics. Custom dashboards for business KPIs.
Infrastructure
Terraform, Helm, Docker, Kubernetes. Deployed on Azure, AWS, GCP, or on-premises. Full IaC, no manual configurations. Reproducible across environments.
CI/CD
GitHub Actions, GitLab CI, Azure DevOps. Automated testing, model validation, deployment pipelines. Canary deployments and rollback strategies for AI systems.
Data processing
Apache Airflow, dbt, custom ETL pipelines. For data ingestion, transformation, and indexing. Batch and streaming support depending on requirements.
Security
Microsoft Presidio for PII. HashiCorp Vault for secrets. Network policies, RBAC, encryption at rest and in transit. Security hardening as standard, not optional.
Why us
AI engineers who understand infrastructure
DevOps DNA
We're not a pure AI consultancy. We come from DevOps and platform engineering. That means every AI solution we build comes with proper IaC, CI/CD, monitoring, and production operations from the start. No Jupyter notebooks in production.
Security by default
PII filtering, network isolation, encryption, RBAC, audit logging. These aren't add-ons we charge extra for. They're built into every project because that's how infrastructure should work.
Honest about feasibility
If AI isn't the right solution for your problem, we'll tell you. If the ROI doesn't justify the investment, we'll say so in the needs assessment. We'd rather lose a project than build something that doesn't deliver value.
Full ownership handoff
When we're done, you own everything: source code, infrastructure, documentation. No proprietary dependencies, no ongoing license fees, no lock-in. Your team can run, modify, and extend the solution independently.
Works with our other AIOps services
Custom solutions that integrate with the full stack
Start with AIShield
Not sure where AI can help most? An AIShield audit maps your current AI usage and identifies the biggest opportunities and pain points. The findings become the brief for your AIForge project.
Learn about AIShield →Run through AIWorkspace
Custom solutions can run through AIWorkspace's security layer. Same DLP filtering, same cost tracking, same audit logging. One platform for all your AI, whether it's chat, agents, or custom applications.
Learn about AIWorkspace →Frequently Asked Questions
Common questions about custom AI projects
How long does a typical project take?
How much does it cost?
Can we use our existing data without moving it?
Do we need GPUs or special hardware?
What if the PoC doesn't work?
Can our team maintain it after handoff?
Do you build chatbots?
Can we start small and expand later?
Have a specific problem AI could solve?
Book a 30-minute call. Describe the problem, and we'll tell you whether custom AI is the right approach, what it would take, and what results you can expect.