Services
We do three things well: cloud infrastructure, model training, and LLM automation. If your problem fits one of these, we can probably help.
Infrastructure That Works
We set up and maintain cloud infrastructure using IaC. CI/CD, containers, observability, security — the stuff that keeps your product running while your team ships features.
Infrastructure as Code
Terraform, Pulumi, CloudFormation. Reproducible infra across AWS, GCP, and Azure. No more clicking around in consoles.
CI/CD Pipelines
GitHub Actions, GitLab CI, ArgoCD. Push code, tests run, it deploys. If it breaks, you know before your users do.
Containers & Orchestration
Docker, Kubernetes, ECS. Service mesh, auto-scaling, multi-environment deploys. The boring but critical stuff.
Observability
Grafana, Prometheus, OpenTelemetry. Logs, metrics, traces — so you actually know what's happening in prod.
Security & Compliance
Secrets management, IAM, network segmentation. Not an afterthought — baked into every deployment from day one.
Data Services
Database provisioning, backups, replication, migration. PostgreSQL, Redis, DynamoDB — automated and monitored.
Custom Model Training
We fine-tune open-source LLMs on your data. Two approaches depending on what you need — LoRA for most cases, full training when it matters.
LoRA Fine-Tuning
Adapt a model without retraining it from scratch
We take an existing open-source model and fine-tune it on your data using LoRA. It's faster, cheaper, and good enough for most production use cases.
What you get
- Works with LLaMA, Mistral, Qwen, Gemma, and others
- Trains on your data with modest compute requirements
- Mergeable adapters — swap them without redeploying
- Dataset to deployed model in days, not weeks
- Quantization-ready outputs (GGUF, AWQ, GPTQ)
Works well for
- Domain-specific assistants (legal, medical, finance)
- Code generation for internal frameworks
- Tone and style alignment
- Classification and extraction pipelines
Full Fine-Tuning
When LoRA isn't enough
Full parameter training for cases where you need the model to deeply learn your domain. More expensive, more time, but sometimes it's the right call.
What you get
- Multi-GPU / multi-node training setups
- Data pipelines with filtering and deduplication
- Evaluation suites and benchmark tracking
- Distributed training with DeepSpeed / FSDP
- Model distillation when you need a smaller version
Works well for
- Building your own foundation model
- High-stakes domains where accuracy is non-negotiable
- Multi-task models with complex reasoning
- On-premise deployments with strict data rules
LLM Automation Flows
We build workflows that use LLMs to automate things that today require manual review — document checks, process enforcement, data cleanup. Structured, logged, and with fallbacks.
Document Validation
Cross-reference documents, extract data, flag inconsistencies. Replaces hours of manual review with repeatable checks.
Process Enforcement
Workflows that follow your business rules step by step — routing, checklists, compliance gates. Nothing skips a step.
Quality Checks
Multi-stage review where LLMs check outputs against your criteria, escalate edge cases, and log every decision.
Agentic Pipelines
Multi-step agents that use tools and talk to external systems. With guardrails and human-in-the-loop when it matters.
Data Enrichment
Classify, normalize, and enrich raw data with LLMs. Turn unstructured inputs into clean datasets your systems can use.
Custom Orchestration
When none of the above fits exactly, we build custom flow engines connecting models, APIs, and your business logic.
How we approach it
Understand
We look at your current process, find what's slow or error-prone, and figure out where an LLM actually helps.
Build
We wire up the flow with checkpoints, fallbacks, and logging. Nothing runs without visibility.
Validate
We test against your real data, tune what needs tuning, and deploy when it's actually ready.