Product engineering, with intelligence built in
Five disciplines, one pod, one price. Everything a small or mid-size company needs to run an AI ecosystem — without hiring an enterprise-scale team to keep it alive.
- Multi-agent orchestration
- Tool + MCP interfaces
- Memory & state design
- Human-in-the-loop UX
- Eval harnesses
- Next.js / React
- Mobile (RN, Swift, Kotlin)
- Event-driven backends
- Cloud & IaC
- SOC2-ready delivery
- Streaming + batch pipelines
- Vector & hybrid search
- Data contracts
- Lakehouse modelling
- Lineage & quality
- Agent interaction design
- Design systems
- Prototyping
- Research & testing
- Architecture review
- AI readiness audit
- Cost & latency teardown
- Remediation roadmap
The stack we work in
MODELS & AGENTS
- Anthropic Claude
- OpenAI
- Open-weight (Llama, Qwen)
- LangGraph
- Model Context Protocol
DATA
- Postgres + pgvector
- Kafka / Kinesis
- dbt
- Snowflake / Databricks
- OpenSearch
PLATFORM
- AWS · GCP · Azure
- Kubernetes
- Terraform
- Temporal
- GitHub Actions
PRODUCT
- TypeScript / Next.js
- React Native
- Python / FastAPI
- Go
- OpenTelemetry
Ways to work with us
Three shapes. All of them end with code in your repo.
- ENTRY
Scoping sprint
Two weeks to decide whether the agent is worth building — and what it would cost to run.
- Workflow forensics
- Feasibility + cost model
- Reference architecture
- Go / no-go memo
- MOST COMMON
TekPod
Tekvo’s standing pod: infrastructure, agents, and operations owned end to end until the system runs itself.
- Product + design + engineering
- Weekly demo cadence
- Your repo, your cloud
- Eval harness included
- Handover documentation
- ONGOING
Operate & evolve
We run the system with you: on-call, evals, model upgrades, and cost tuning.
- SLO-backed operations
- Quarterly model reviews
- Cost optimization
- Roadmap partnership
FAQ
What people ask before signing
What does a TekPod cost?
A TekPod is priced monthly and runs 8 to 24 weeks, sized for a company of 20 to 500 people rather than an enterprise budget. The entry point is a fixed-fee two-week scoping sprint that ends with a cost model and a go/no-go memo, so you decide with numbers instead of a proposal.
Do we need to hire an ML team first?
No. A TekPod is the standing team: we stand up the GPU and cloud infrastructure in your account, build the agents for your use cases, and run the whole thing in production. You never hire an ML platform team, own the GPU capacity problem, or staff a 3am on-call rotation.
Can you rescue a pilot that stalled at 80%?
Yes — it is a named engagement. We start with an architecture review, an AI readiness audit, and a cost and latency teardown, then deliver a remediation roadmap. Most stalled pilots are missing an eval harness and a policy layer, not a better model.