For AI Startups

AI startups need different architecture.

AI startups need architecture that handles inference latency, cost optimization, evaluation frameworks, and prompt management as infrastructure, not bolted-on features.

Vector databases have different access patterns. Model serving has different performance requirements. Evaluations need reproducibility. Cost control is existential.

For AI founders who need a backend that thinks about inference economics, and for venture studios building multiple AI products. $45–75/hr. Transparent. Founder-led. Architecture-first.

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Token-level accounting Inference-first $45–75/hr
Why Different

Backend designed for model serving economics.

Most AI teams build on generic web frameworks then optimize later. We build for vector search, token accounting, and inference cost containment from architecture. Every request is instrumented. Every token is logged. Every inference is billable. Fallback models are ready before latency spikes.

What gets built

Vector database abstraction layers that optimize for your embedding model
Request routers that pick the cheapest API endpoint (OpenAI, Claude, Llama) based on input complexity
Prompt versioning systems that track A/B test results at token cost granularity
Eval frameworks that run automated testing on every model update
What We Build

Full-stack AI systems.

Vector Infrastructure

Pinecone, Weaviate, Milvus abstraction. Embedding refresh pipelines. Semantic caching. Range query optimization.

Model Serving

Multi-model routing (OpenAI, Claude, Llama, local). Batch inference for cost savings. Context length optimization. Token budgeting.

Prompt Testing

A/B testing infrastructure. Prompt version control with git-like history. Cost tracking by prompt version. Output evaluation scoring.

Eval Frameworks

Automated test suites for model behavior. Hallucination detection. Regression testing on model updates. Benchmark tracking.

RAG Pipeline

Document ingestion and chunking. Retrieval optimization. Prompt augmentation from knowledge base. Citation tracking.

Observability Stack

Token-level logging. Latency tracking by model. Cost per request. Error rate by endpoint.

Proof

AI startups at scale.

50M+
Inference Calls Processed
21
Funded AI Startups
At Scale

Backend that scales with your model portfolio.

Multi-Tenant Inference

Isolated token budgets per customer. Per-tenant model preferences. Cost attribution. Billing by token.

Cost Optimization

Model selection by latency SLA. Batch processing for off-peak. Context caching. Prompt compression.

Fine-Tuning Pipeline

Dataset management. Training infrastructure. Model versioning. Performance comparison vs. base model.

Governance & Compliance

Audit logging. Data retention policies. PII masking. Model explainability tracking.

Ready

Ready to build AI products that don't burn money.

Token accounting. Model routing. Cost attribution. Let's ship it.

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Questions

Frequently asked questions about our AI startup engineering

Direct answers about how this engagement actually works. If your question is not here, ask Mohit directly.

How do you handle token cost accounting at scale when we're running multiple models?
Every inference gets logged at token granularity with model ID, prompt version, and cost attached. You route requests through a cost router that tracks spend per customer, per feature, per model. When you hit unexpected costs, the audit trail tells you exactly which prompt version triggered it. We've found most teams don't know their true cost per feature until this is built.
We're torn between fine-tuning and RAG. How do we know which approach will work for our use case?
Fine-tuning is about behavior change. RAG is about data freshness. If your problem is that the model doesn't know your specific format or reasoning, fine-tune. If it's that your data changes weekly, RAG. Most teams need both at different layers. The 48-hour audit includes a technical recommendation for your specific constraints.
What's a typical engagement timeline and cost for an AI startup at our stage?
Depends on whether you need full-stack (backend, inference routing, cost tracking, evals) or just the inference layer. A cost-control architecture with multi-model routing and token budgeting typically runs 8-12 weeks and costs $35K-60K at $45-75/hr. Smaller engagements start at sprint basis (2-4 weeks per sprint).
Do you have experience shipping AI products, or is this mostly general backend work?
We've shipped 12+ AI products to production, including a vector database migration that cut query latency by 70%, batch inference pipelines that saved one team $40K monthly in inference costs, and prompt evaluation frameworks that reduced regression by 80%. Several of those teams are now funded.
How do we know if your architecture approach is right before we commit to a full engagement?
The 48-hour paid audit ($3,500) covers model selection rationale, cost routing design, token budgeting, and a report on whether you should build in-house or outsource. You'll know exactly what you're paying for and what the risk is if you don't address it.
Who do we talk to during the engagement? Will we stay in sync with product and engineering decisions?
Mohit leads the initial architecture phase and design reviews. Your founding team stays integrated throughout. We work in sprints with weekly syncs. You own the code and all IP transfers on completion. NDA available if needed.

Have a different question? Email the team or read the full FAQ.