We build the AI layer
for your product.
Inference architecture, model routing, and cost optimization for startups that can't afford to get it wrong.
// Before Vector TC
const response = await openai.chat({
model: "gpt-4", // $30/1M tokens
messages: [...prompt],
});
// latency: 4,200ms cost: $0.18/req
// After
const response = await inference({
task: "analysis", // auto-routed
cache: true, // prompt cached
});
// ttft: 340ms cost: $0.003/req
The problem
Most startups learn these lessons after they ship.
01
Inference costs spiral at scale
You prototype with GPT-4. It works great. Then you hit 10k users and your AI bill becomes your biggest line item. The model that made sense at demo day is the wrong model for production.
02
Latency kills user experience
A 4-second AI response feels broken. Users churn. Most teams don't know about streaming, prompt caching, or model routing until they've already shipped a slow product.
03
Model lock-in slows you down
Building tightly coupled to one provider means you can't switch when a better model ships — and better models ship every few months. Abstraction isn't optional, it's how you stay current.
Services
What we do for early-stage teams.
Inference Architecture
Design your AI stack from the ground up. Model selection, provider configuration, fallback logic, and cost controls built in before you write a single product feature.
Model Routing & Orchestration
Route requests to the right model for each task — cheap models for simple queries, powerful models for complex ones. Build multi-model pipelines that reduce cost without sacrificing quality.
Cost & Latency Optimization
Audit your existing AI pipeline. Find where you're overpaying, where latency is hiding, and implement fixes: caching, batching, quantization, model distillation.
Full AI Feature Builds
From zero to shipped. We design and build the entire AI feature — architecture, prompts, inference pipeline, evaluation, monitoring — so your team can move on to the next thing.
Proof of work
Things we've shipped.
Credit Capsule
Automated AI video pipeline for YouTube Shorts
Multi-model production pipeline: GPT-4o-mini generates scripts, ElevenLabs handles voice, Whisper transcribes captions, FFmpeg renders the final video. Runs on a launchd schedule and publishes directly to YouTube — no manual steps between idea and upload.
PR Contribution Agent
AI agent that measures engineering team contributions via GitHub
Connects to GitHub repos via the API, pulls PR history per team member, and uses Claude to synthesize contribution patterns — volume, review activity, PR size distribution, and merge rate — into per-contributor reports. Gives engineering managers a fair, data-backed picture beyond raw commit counts.
Fintech Crew
Multi-agent system for financial analysis
Agents collaborate via tool use to fetch, analyze, and synthesize financial data. Each agent owns a specific task — data retrieval, ratio analysis, narrative generation — and hands off to the next without human intervention between steps.
Why Vector TC
We've already made
the expensive mistakes.
Three AI products in production. We've hit the cost spikes, the latency walls, and the model-switch scrambles ourselves — so we know exactly where the problems hide.
Technical depth
We've built production AI pipelines — not consulted on them. Every recommendation comes from code we've shipped.
Inference-first thinking
We design around constraints: latency budgets, cost ceilings, provider SLAs. The architecture fits your product, not the other way around.
Model-agnostic
No provider allegiance. We pick what's right — Claude for reasoning, GPT-4o-mini for cost, Gemini Flash for speed — and route between them.
Startup pace
Engagements are short, scoped, and actionable. You get working code and clear next steps, not a 60-page strategy deck.
Ready to start
Let's talk about
your AI stack.
Tell us what you're building and where AI fits in. We'll take a look and get back to you.
@contact@vectortc.com