# Paiteq — Enterprise AI Engineering > Paiteq builds production AI — agents, RAG, LLM apps, automation, generative AI. Eval-first, senior-led, fixed-scope engagements. Same-day reply from engineering. **HTML version:** https://www.paiteq.com/ ## Key facts - Offices: Bengaluru, IN and Dallas, TX, US. - Engagement model: fixed-scope audits and pilots. Walk-away clause on every audit. No open-ended retainers. - Eval-first: every shipped system carries a frozen eval set and a defined kill point. - 11 service pillars across agents, RAG, LLM apps, ML, MLOps, automation, RPA, chatbots, generative AI. - 6 industry verticals: ecommerce, fintech, healthcare, insurance, logistics, SaaS. - Contact: info@paiteq.com · +91 80 5003 2994. ## Related pages - [Services hub](https://www.paiteq.com/services/) - [Case studies](https://www.paiteq.com/case-studies/) - [About](https://www.paiteq.com/about/) - [Contact](https://www.paiteq.com/contact/) - [Blog](https://www.paiteq.com/blog/) ## About Paiteq Enterprise AI engineering — production agents, RAG, LLM apps, automation, generative AI. Eval-first, senior-led, fixed-scope engagements. Same-day reply from engineering. NDA counter-signed before discovery. Walk-away clause on every engagement. **Site index for agents:** https://www.paiteq.com/llms.txt **Full content for agents:** https://www.paiteq.com/llms-full.txt **Book a call:** https://www.paiteq.com/contact/ --- ## Full content Enterprise AI Engineering # The *AI development company* production teams trust to ship. Paiteq delivers AI development services end-to-end — production AI agents, RAG pipelines, LLM apps, intelligent automation, generative AI, and custom AI software. Eval-first, senior-led, fixed-scope engagements. [Talk to engineering](/contact/) [See how we work](#process) Production capability 180+ systems shipped Engineering surface - RAG Architecture - Fine-tuning / LoRA / PEFT - Eval Harness + Red Team - vLLM · TGI · SGLang - Agent Orchestration - Vector DB + Hybrid Search - Multimodal · Vision + Audio - HIPAA · SOC 2 · GDPR - On-prem / VPC Deployment - RAG Architecture - Fine-tuning / LoRA / PEFT - Eval Harness + Red Team - vLLM · TGI · SGLang - Agent Orchestration - Vector DB + Hybrid Search - Multimodal · Vision + Audio - HIPAA · SOC 2 · GDPR - On-prem / VPC Deployment 001 / TEAM ## The team behind Paiteq has shipped software since 2010. 15+ years of combined engineering. Hundreds of products built across mobile, web, and infra. We grew up as a software shop and turned into an AI development company once production AI stopped being a research story — now focused on sales agents, RAG systems, multi-agent orchestration, and the eval discipline that gets them into production. 0 + Years team experience Engineering, shipping, on-call 0 + Engagements shipped Across our portfolio of brands 0 + Engineers AI · ML · backend · ops 0 + Agents in production Sales · support · ops · research 002 / OUR PRODUCTS ## We don't just consult — we operate the platforms. Two of our own products run in production. They're the credibility behind the engineering we sell. AEROSTACK AI platform · primary ### An AI platform *powering* agents and chatbots at scale. Paiteq's flagship AI product. **100+ teams** use Aerostack to ship production agents in days, not months — onboarding the next **1,000** over the following twelve months. The same primitives power every client engagement we lead. - **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 - **Tool surface ready** — CRM, ticketing, web search, code, custom APIs - **Observability + rollback** — Langfuse-grade traces, one-click rollback [See Aerostack](https://aerostack.dev) [Request a demo](/contact/?topic=aerostack-demo) [ NYBURS Consumer · social ### A large-scale social media product, operated by the same team. Nyburs is the team's consumer social app — production traffic, real-time workloads, and the infra discipline that comes from shipping a product at scale. Same engineers, same on-call rotation, same rigour you get on a Paiteq engagement. live ops Learn more → ](https://www.nyburs.com) 003 / NUMBERS ## How we measure what we ship. The four metrics that gate every production deploy. Scored against the eval set in week 2. 0 % Task success rate Eval-set graded, weekly < 0 % Hallucination rate LLM-judge + human spot-check 0 –4w Pilot duration Fixed scope, fixed price 0 % Pilot → Build rate Pilots that graduate to production 004 / PRACTICES ## Twelve AI development services, one engineering org. Each practice is owned by senior engineers with production experience. Same build process and engagement shapes whether you hire us as an AI development company for a single agent or for a full multi-team platform. [All services →](/services/) [ 01 / AI ↗ AI Agent Development Autonomous, tool-using AI agents for production workloads. Plan/ActToolsMemory ](/services/ai-agent-development/)[ 02 / RAG ↗ RAG Development Retrieval-augmented generation systems with evaluation built in. HybridRerankEval ](/services/rag-development/)[ 03 / LLM ↗ LLM Development Custom LLM apps — RAG, fine-tuning, evaluation, deployment. Fine-tuneAgentsEval ](/services/llm-development/)[ 04 / AI ↗ AI Workflow Automation Intelligent workflows on n8n, Make, and custom agent orchestration. n8nMakeCustom ](/services/ai-workflow-automation/)[ 05 / GENERATIVE ↗ Generative AI GenAI products end-to-end — text, image, multimodal, OpenAI/Claude/Gemini. DiffusionMultimodal ](/services/generative-ai/)[ 06 / MACHINE ↗ Machine Learning Custom ML — training, serving, MLOps. MLOpsRankingForecast ](/services/machine-learning-development/)[ 07 / AI ↗ AI Consulting AI strategy, audits, roadmap. StrategyAudit ](/services/ai-consulting/)[ 08 / CHATBOT ↗ Chatbot Development Production chatbots on LLMs with guardrails and observability. RAGToolsVoice ](/services/chatbot-development/)[ 09 / RPA ↗ RPA Development Intelligent automation — beyond rule-based RPA. WorkflowUiPath+ ](/services/rpa-development/)[ 10 / INTEGRATION ↗ AI Integration Drop-in AI for existing apps — OpenAI / Anthropic / Vertex. OpenAIAnthropicVertex ](/services/ai-integration/)[ 11 / MIGRATION ↗ AI Migration Legacy software → AI-modernized stack. Eval-validated cutover. CutoverEval ](/services/ai-migration/)[ 12 / MLOPS ↗ MLOps Deploy, monitor, scale ML and LLM systems in production. DeployMonitorScale ](/services/mlops/) 005 / PROCESS ## Six steps from discovery to running. Same process whether it's a 2-week pilot or a 16-week production build. The gates change in depth, not in shape. WEEK 1 ### Discovery Map the workload, scope the surface, identify the eval set. WEEK 2 ### Spec Stack picks, prompts, guardrails. Eval set graded by domain expert. WEEK 3–6 ### Prototype First runnable version graded against the eval set. WEEK 6–10 ### Eval gates Task success, hallucination, latency all green before deploy. WEEK 10+ ### Deploy Auth, observability, rate limits, rollback playbook. ONGOING ### Running Weekly eval runs, prompt iteration, regression alarms. 006 / INDUSTRIES ## Eight verticals we've shipped into. Domain knowledge isn't extra — it's the difference between an agent that ships and one that hallucinates against your regulations. We pair AI engineers with subject-matter experts for every engagement. 01 B2B SaaS Sales agents, internal copilots, support deflection, churn-prediction. Where most of our agent volume ships. Outbound research · Slack ops 02 Health-tech Clinical Q&A, prior-auth automation, intake triage. PII-scrubbed by default. HIPAA-aligned engagements. RAG over clinical docs 03 Manufacturing Invoice + PO routing, supply-chain agents, predictive maintenance on sensor data. AP automation · CMMS triage 04 Fin-tech Risk-scoring assistants, compliance Q&A over regulations, KYC and onboarding agents. Reg Q&A · onboarding 05 Legal Contract Q&A, clause extraction, redline review. Domain-expert-graded eval sets. MSA Q&A · redline 06 E-commerce Catalog enrichment, AI search + recommendations, agent-driven checkout flows. Product extraction 07 Ed-tech Tutoring agents, content generation, voice narration with low-latency turn-taking. Tutoring · TTS narration 08 Logistics Routing agents, shipment Q&A, claims triage. Tool-call accuracy is the eval anchor. Claims · ETA Q&A 007 / WORK ## Where teams have shipped. Anonymized featured engagements. Industry and segment are real; metrics are real; brand names removed under NDA. [More →](/case-studies/) Sales B2B SaaS · 11–50 emp ### Lead-qualification + outbound research agent Multi-step research over public signals + ICP scoring. Drafts personalised first-touch, escalates above threshold. 0 SDR seats Support Health-tech · enterprise ### Tier-1 deflection agent RAG over docs + ticket archive. Handles password, billing, onboarding. Clinical escalations carry full context. 0 % p1 tickets Ops Mfg · 200+ emp ### Invoice matching + AP routing agent OCR + LLM extraction → match against open POs → route to approver. Exceptions to ops lead with annotated diff. <6 months in 008 / WHY PAITEQ ## Three things teams remember about working with us. - 01 ### Eval-first The graded eval set lands in week 2 — before the first prompt is written. Every iteration is measured against it. No production wire-up until thresholds are green. - 02 ### Senior-led The engineer who shows up to the first call leads the build. No SDR funnel. First reply on every inbound is same-day from someone who could ship the agent. - 03 ### Fixed scope Every engagement has a fixed end-date and a stop option. Pilots are 2–4 weeks. Builds are 8–16. You always know what's coming, when, and what counts as done. 008b / AI SOFTWARE ## Why teams pick Paiteq as their *AI software development company*. We're not a platform reseller and we don't sell hours. Paiteq is a full-stack AI software development company — architecture, build, eval, deploy, run — on the same team. Custom AI software built for your workload, owned by you, shipped with the same engineering rigor production SaaS teams expect from their core systems. AI-native builds Custom AI software built ground-up around the AI workload — not retrofitted onto a CRUD app. Architecture choices follow the data, the latency budget, and the eval surface, not the convenience of an existing stack. Engineering discipline Code review, CI, observability, on-call runbooks, regression alarms — the same disciplines a senior SaaS team would apply to a payments service, applied to your AI system. Eval gates are a first-class part of the deploy pipeline, not an afterthought. You own everything Code, prompts, fine-tuned weights, eval sets, infrastructure-as-code — all transferred into your repository under the SOW. No vendor lock-in, no platform tax. We retain only the engineering learnings for our internal playbook. Production from day one Auth, rate-limit, observability, fallback policies, cost guardrails baked into every deploy. The system that ships to production is the one we built — not a notebook that needs another team to "productionize." Same engineers from architecture to on-call. 009 / ENGAGE ## Three engagement shapes. Pilot, Build, Run. Pilots and Builds are fixed-scope and fixed-duration. Run is a separate monthly SOW for teams that want continued iteration. 01 FIXED SCOPE ### Pilot 2–4 weeks One scoped agent, end-to-end against your data, with the eval set graded by a domain expert. - One use case, real integrations - Eval framework (30–50 graded examples) - Working prototype + memo for next phase [START WITH PILOT →](/contact/?topic=pilot) 02 FIXED SCOPE ### Build 8–16 weeks Production build with eval gates, observability, integrations, and post-launch iteration. - Everything in Pilot - Auth · rate-limit · observability - Eval gates baked into deploy - 4 weeks post-launch iteration [START WITH BUILD →](/contact/?topic=build) 03 TIME & MATERIALS ### Run Monthly Ongoing iteration, eval-set maintenance, prompt + tool updates as your data and workflows evolve. - Weekly eval review - Drift + regression alarms - Prompt + tool iteration - Quarterly architecture review [START WITH RUN →](/contact/?topic=run) 010 / STACK ## The frameworks we build on. Stack choices follow workloads, not house preferences. We work in whatever framework makes the agent ship — including ones we'll only learn the week your engagement starts. - LangChain - LangGraph - CrewAI - AutoGen - DSPy - Composio - OpenAI - Anthropic - Pinecone - Qdrant - LiveKit - Langfuse - LangChain - LangGraph - CrewAI - AutoGen - DSPy - Composio - OpenAI - Anthropic - Pinecone - Qdrant - LiveKit - Langfuse > Most projects that fail in production fail because the team picked the wrong shape — not because they picked the wrong model. Architecture before vendor. Paiteq engineering From the blog — AI agents vs. chatbots 011 / COMPLIANCE ## Built for enterprise from day one. Default posture is SOC 2 + ISO 27001 aligned. Regulated engagements (HIPAA, GDPR, EU AI Act) get the evidence work baked into the SOW — no rework at the security review. Audited annually · Continuous monitoring - SOC 2 Type II Audited annually AUDITED · 2026 - ISO 27001 Information security mgmt AUDITED · 2026 - HIPAA-ready Health-tech engagements READY - GDPR / EU AI Act EU client deployments READY 012 / FAQ ## Common buyer questions. If the answer you need isn't here, the contact form is faster than a meeting — first reply is same-day from an engineer. How much does an AI agent cost? Pilots run 2–4 weeks at fixed price (low-five-figures typical). Production builds with eval gates, observability, and integrations run 8–16 weeks. We share specific bands during the first call. Open-ended T&M only on the Run phase, not on Pilot or Build. How long does it take to ship a production AI agent? Pilot in 2–4 weeks. Full custom build in 8–16. Multi-agent and voice systems run longer (10–20 weeks) because of orchestration and latency tuning. Every engagement has a fixed end-date — you always know what's coming. Should we build in-house or work with Paiteq? Build in-house when AI is your core product and you have senior AI engineers already on staff. Work with us when AI is enabling work — when shipping fast and getting the eval methodology right matters more than long-term ownership of the team. Most clients use us to ship the first 2-3 systems, then hire to scale. What frameworks and models do you build on? Stack choice follows the workload. LangGraph for stateful agents, CrewAI for multi-agent supervisor / worker, Vercel AI SDK or OpenAI Agents for simpler tool-calling, Composio when the tool surface is large. Models: Claude, GPT-4o, Gemini for hosted; Llama / Mistral / Qwen for self-hosted. We benchmark 2 options against your eval set before lock-in. Will the agent work with our existing systems? Yes — that's most of the engineering work. We integrate against CRMs (Salesforce, HubSpot), ticketing (Zendesk, Intercom), data warehouses (Snowflake, BigQuery), and custom internal APIs. Tool-call accuracy against your real systems is one of the four eval metrics we gate on. Who owns the code, prompts, and eval sets? You do. All artifacts transfer into your repository under the SOW. We retain no rights to your prompts, eval data, or fine-tuned weights. Paiteq keeps the engineering learnings — patterns, methodologies — for our internal playbook. 013 / BLOG ## From the engineering blog. Deep technical writing on the things we build every day — agents, RAG, evaluation, framework trade-offs, production failure modes. [All posts →](/blog/) - [ ![Fraud-detection decision flow](https://cdn.sanity.io/images/xr290ucr/production/2cbfb1035c2609046fc446980cffb1ab62c5cf58-1408x768.png?w=600&q=70&auto=format&fit=max) Agents ### AI Fraud Detection at the Auth Boundary: Operator Architecture (2026) Auth-boundary fraud detection done eval-first: hybrid rules + ML + LLM with audit logs, false-positive cost math, and the production architecture we ship. With a walk-away clause. Navin Sharma May 22, 2026 19 min ](/blog/ai-fraud-detection-at-auth-boundary/) - [ ![Frost-crystal lattice radiating from a central node — orchestration patterns in a multi-agent system](https://cdn.sanity.io/images/xr290ucr/production/73b0c0032d61a220527e6ff1e54f3c053cd3709b-1408x768.png?w=600&q=70&auto=format&fit=max) ### Multi-agent orchestration patterns: a 2026 production guide Six multi-agent system patterns that actually ship in 2026 — supervisor, swarm, hierarchical, blackboard, sequential, hybrid — with framework picks and the production failure modes nobody warns you about. Navin Sharma May 17, 2026 27 min ](/blog/multi-agent-orchestration-patterns/) - [ ![Macro photograph of frost dendrites on cold glass — the branching retrieval pattern of a customer service chatbot](https://cdn.sanity.io/images/xr290ucr/production/19608b3fa221073ea4d065bef54b13ee71b7c720-1408x768.png?w=600&q=70&auto=format&fit=max) ### Customer service chatbot: a 2026 buyer's guide A 2026 buyer's guide to customer service chatbots — RAG over your docs, eval gates on deflection, and what the LLM tier actually costs in production. Navin Sharma May 17, 2026 13 min ](/blog/customer-service-chatbot-buyers-guide/) Start a project ## Let's *build* something that ships. Pilot in 2–4 weeks. Custom build in 8–16. Same-day response on every inbound. [Talk to engineering](/contact/) [Explore services](/services/)