LLM apps, RAG pipelines, agents, voice systems, fine-tuning. Hands-on across Anthropic Claude, OpenAI, open-weight models on vLLM, and Bedrock for regulated workloads.
About
We engineer AI like it's infrastructure.
Paiteq is an AI engineering company. Every engagement ships a working system with an evaluation framework that proves it works — not a deck and a roadmap. Engineering reads every inbound. Walk-away clause on every audit.
001 / WHY PAITEQ EXISTS
Four things we hold non-negotiable.
Most AI engagements fail in the same predictable ways: no eval set, sales translating for engineering, walk-away clauses that don't survive contact with revenue targets, and IP arrangements that surprise the buyer at handover. We hold the opposite as load-bearing.
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01 The eval set is the deliverable
Most AI projects fail because nobody agreed what success looked like before the code shipped. We write the eval set in week 2, with your domain expert grading. Production wire-up waits until the thresholds turn green. No eval set, no engagement.
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02 Engineering reads every inbound
The first reply to any inquiry comes from an engineer who could lead the build, not a salesperson translating between you and the team. The conversation goes as deep as the workload needs — short for small scopes, long when the scope is real.
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03 Walk-away clause is not theatre
Roughly 1 in 5 audits we run end up recommending no AI work, or recommending you defer six months until a prerequisite is in place. You keep the audit deliverable. That's the whole point of separating the audit fee from any pilot money.
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04 You own everything we build together
Code, prompts, eval sets, deployment scripts, ops runbook — all transfer to your repo on engagement close. We retain the right to reuse operator patterns (how to ship a tier-1 deflection agent, how to structure a RAG eval) but not your prompts, your data, or your code.
002 / HOW WE WORK
Audit → Pilot → Continuous. Stop after any phase.
Three engagement shapes, one walk-away clause on each. The audit is priced separately from the pilot so the recommendation can honestly be "don't build" without burning the engagement.
- 01AUDIT 1–2 weeks · fixed-fee
Workload map + model picks + cost projection
We walk every source, every decision point, every handoff in the workload you brought us. Output is a workload map, a per-task model recommendation with reasoning, a token-cost projection against expected traffic, and a 90-day roadmap with ranked sequencing. If the recommendation is no AI, you keep the deliverable.
- 02PILOT 4–8 weeks · fixed-price
One workload shipped, with the eval suite that proves it works
We pick the one workload from the roadmap that has the cleanest evaluation surface and the highest leverage. Eval set ships in week 2 with your domain expert grading. Production wire-up waits until the thresholds are green. You get the working system, the monitoring, and the ops runbook for the on-call team.
- 03CONTINUOUS Monthly · cancel any month
Embedded squad shipping the next workload on your roadmap
If the pilot moves the metric, we embed for the next workloads in the audit roadmap. Same model picks, same eval discipline, same ownership transfer at each ship. Monthly billing, no lock-in. About 60% of pilots convert to continuous; the ones that don't usually mean the audit was wrong, not the squad.
003 / TEAM
15+ years across the team. Cross-disciplinary by design.
Production AI is rarely "just the model". The system that ships is model + retrieval + eval + orchestration + UX + ops. The team carries the full stack so we can move judgment between layers without renegotiating who owns what.
React Native and Flutter shipping production. The AI-app surface lives in someone's app — we ship the surface too, not just the backend.
Python, Node, Go, Postgres, vector stores. AWS, GCP, Cloudflare Workers. We pick boring infrastructure for AI workloads — the novelty budget is for the model layer.
Eval rubrics, observability dashboards, agent-facing UX. AI surfaces fail more on UX (latency, fallback, refusal) than on model accuracy. We design for those failure modes upfront.
The team has shipped across DTC retail, fintech, healthcare-adjacent, ed-tech, B2B SaaS, and content / publishing. Sector experience matters more for the discovery call than for the build — the build is mostly the same craft regardless of vertical.
004 / SELECTION
What we take. What we deflect.
Being honest about scope upfront saves both sides a quarter. The list on the right is not a moral position — it's a list of work where we can't ship a measurable result, or where the harm-to-value ratio is wrong.
We ship
- Production LLM apps with eval gates
- RAG pipelines on Pinecone, Qdrant, Weaviate, pgvector
- Single-agent and multi-agent systems with tool use
- Voice agents on OpenAI Realtime, Claude Realtime
- Workflow automation with LLM-as-judge nodes
- Generative pipelines on Flux, SDXL, Sora, Runway
- Classical ML where ML is the right answer
- Fine-tunes on Llama 4, Mistral, smaller domain models
- Architecture reviews and eval-set authoring
We deflect
- Slide-deck consulting without a build
- Research POCs with no path to production
- AGI claims or vague 'AI transformation' work
- Projects where nobody can grade what good looks like
- Deepfakes, non-consensual likeness, election content
- Wholesale outsourcing of judgment-critical decisions
- Engagements priced on team size, not on workload
- Custom foundation-model training (use the ones that exist)
- Anything we can't ship a measurable eval gate on
005 / STACK + COMPLIANCE
Model-agnostic by stance. Honest about compliance.
The stack pick lives at the workload level, not the vendor level. Compliance posture is the same: we'll tell you what we hold, what we follow but don't hold, and which procurement gates we can clear today versus which need a partner.
- Model: long-context reasoning
- Claude Sonnet 4.6 (default)
- Model: realtime voice
- OpenAI Realtime · Claude Realtime
- Model: cost-sensitive batch
- Llama 4 / Mistral on vLLM
- Model: regulated workloads
- Anthropic on AWS Bedrock + BAA
- Vector DBs
- Pinecone · Qdrant · Weaviate · pgvector
- Eval harness
- Inspect AI · RAGAS · Langfuse
- Orchestration
- n8n · Inngest · Temporal
- Cloud
- AWS (primary) · GCP · Cloudflare Workers
HIPAA-AWARE Production-ready
Claude on AWS Bedrock with BAA and PrivateLink VPC, audit-logged. Field-level masking on PHI before any model call. We've shipped the pattern.
GDPR / EU RESIDENCY Production-ready
EU-region residency on hosted models (Anthropic EU, OpenAI EU data residency, Azure West Europe). DPA workflow + subject-access-request runbook documented at handover.
SOC 2 Partial — vendor-aware
We follow SOC-2-ready practices (audit logs, least-privilege IAM, key rotation, encryption at rest and in transit) but are not ourselves SOC 2 Type II certified as a vendor. If your procurement requires a SOC 2 report from the agency itself, flag it upfront.
ISO 27001 Aligned — not certified
Practices align with the framework. No third-party certification yet. We're transparent about this — agencies that claim more than they hold burn the trust they were hired for.
006 / PRODUCTS WE OPERATE
The products on the Paiteq operator stack.
Paiteq owns and operates two production properties — one B2B platform, one consumer social product. The agency side of Paiteq inherits the discipline of running these every day: failure modes you only learn by being on-call for your own systems, infra economics you only feel when the bill is yours.
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Developer platform on Cloudflare's edge for building AI-powered products — pre-built integrations, serverless functions, agent deployment, and MCP wiring. Describe what you need; Aerostack hands back a production URL.
- Visual agent builder — plan / act / reflect graph, no glue code
- Eval gates baked in — task success, halluc., latency gate every deploy
- Multi-provider routing — Claude, GPT, Gemini, Llama with cost + quality routing
- Observability + rollback — Langfuse-grade traces, one-click rollback
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India's hyperlocal social platform — neighborhood timelines, local-language chat, live streaming, and community problem-solving. Built around the thesis that social value compounds with local proximity, not global scale. This is the team's consumer surface — production traffic, real-time workloads, the infra discipline that comes from shipping a product at scale.
- Neighborhood feeds — geo-scoped timelines, not a follow graph
- 12+ Indian languages — first-class local-language chat, not translation
- Live streaming — real-time WebRTC layer, scaled on edge POPs
- Community problem-solving — moderation + civic tooling baked in
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007 / Have a workload in mind?
Talk to engineering.
The first reply comes from someone who'd be on your build. Same business day on most inbounds.