Which model do you use?

Whichever fits the use case. Claude for nuanced reasoning, GPT for tool-use density, open-weight for cost-sensitive workloads. We benchmark before we pick.

How do you control token costs?

Token budgets per request, per session, and per tenant. Hard limits, soft alerts, and a dashboard your finance team can read.

Usually no — RAG + prompt engineering covers 95% of use cases at a fraction of the cost. We fine-tune only when evals prove the lift is real.

What about prompt injection?

System-prompt isolation, output validation with Zod, and tool-use guardrails. Destructive operations always require confirmation.

AI agents your customers can actually talk to.

Customer-facing chatbots, internal AI assistants, and LLM features integrated into the apps you already ship — typed, observable, and on your stack.

Book a 30-min call See the process

Available for Q3 engagements·Senior team only·Starting from $10K·Reply within 24h

01 / WHAT WE FIX

The blockers we see again and again.

Off-the-shelf chatbots feel generic

The chatbot vendor does not know your product, your data, or your users. Customers can tell.

AI sits outside your product

A floating widget that does not know who the user is, what plan they are on, or what they were trying to do.

No path from demo to production

The internal demo worked. Two weeks later your team is firefighting prompt drift, rate limits, and runaway token costs.

02 / HOW WE FIX IT

The way we ship around them.

AI chatbots that know your product.

Customer-facing chatbots and support agents grounded in your actual docs, accounts, and product state. Not a generic GPT wrapper.

· RAG search over your docs and data
· User + billing context on every reply
· Handoff to human support when needed

AI-driven workflows and automations.

Email triage, document extraction, lead scoring, internal copilots — wherever a human is doing repetitive judgment work, an AI workflow can take 80% of it.

· Tool-using agents with typed schemas
· Approval flows for high-stakes actions
· Audit trail for every decision

AI integrations into the apps you already ship.

Dropped into your existing React, Next.js, Laravel, or mobile app. We own the prompts, the model choice, the observability, the rate limits.

· Streaming responses with citations
· Multi-provider routing (Anthropic, OpenAI, open-weight)
· Per-tenant cost + usage dashboards

Already scoping a ai & automation project?

Book a 30-min call

03 / HOW WE WORK

From spec to production.

A weekly loop with a single point of contact and dated deliverables.

Day 1-2
Use-case scoping
Three candidate features, prioritized by impact vs cost. We pick one to ship and design the rest.
- Use-case doc
- Cost model
- Spec
Days 3-7
Infrastructure + build
Vector store, prompt registry, observability pipeline. We pick the model (Anthropic, OpenAI, open-weight) based on cost and capability.
- Vector index online
- Prompt registry
- Observability dashboard
Days 8-12
Feature completion
The AI feature lands inside your product. Streamed responses, citations, tool-use, the works.
- Live AI feature
- Tool integrations
- Citation UI
Days 13-14
Eval + launch
Eval harness so your team can iterate on prompts safely. Runbooks for rate limits and degradations.
- Eval suite
- Runbooks
- Prompt iteration guide

TECH STACK

Technologies we use.

Backend: Python
AI/ML: Anthropic Claude
OpenAI
Gemini
LangChain
Vercel AI SDK

05 / PROOF

What this looks like in production.

2 weeks

Demo to production

24/7

Technical support

100+

AI agents shipped

Featured · 2026 · Media · Creator economy

Mozenthra

Creator-first short video at streaming scale.

Architected and shipped Mozenthra into a scalable short-video and unlimited cloud-storage platform for creators.

LaravelNode.jsPythonReactNext.js

Read the case study

FAQ

The questions everyone asks.

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