Two senior founders · open for project work

We build AI systems
that survive in production.

Buildgent is a two-founder AI studio. We design, build and ship agentic AI, RAG and document-intelligence systems that hold up in production — by engineers who've cut client workloads ~80% with AI agents and scaled platforms to 9M+ users.

Built at — our prior roles ReBillion· Labcorp· AAVAA· TEKsystems· Leher Track record from our prior roles — Buildgent is our new studio.
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work automated by AI
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users scaled
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peak DAU
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faster AI APIs
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continents
01 The studio

We're two engineers who've shipped AI to millions of users. One builds the brain, one builds the whole product. We ship systems that run in production and cut real cost — not slideware.

AGKS
Anshul Goel & Karan Singla
Founders · Buildgent
Outcomes over output · Production over demos · Hands-on, senior, end to end
02 Services

What we build for you

Agentic AI, document intelligence, full-stack AI products and the backend to scale them — plus fractional AI leadership when you need senior direction, not another contractor. From a stuck prototype to a production SaaS your users pay for.

Agentic AI & LLM Development

Autonomous, multi-agent systems (LangGraph, Vertex AI) that plan, call tools via MCP and act — hardened with retrieval, evals, guardrails and human-in-the-loop so they hold up on real work, not just demos.

For — teams who want AI that does the task, not just chats
LangGraphMCPRAGMulti-agentEvals

Document Intelligence & OCR

Turn messy PDFs, scans and forms into clean, validated, structured data your systems can trust — Textract, Document AI, Mistral and LlamaParse with LLM validation on top.

For — ops-heavy teams drowning in paperwork
TextractDocument AIMistralLlamaParseIDP

Full-Stack AI Product Engineering

Complete AI products end to end — LLM features and RAG on a React + Python/Go/Java stack, with ML in the loop and a UI users actually enjoy.

For — teams shipping a whole product, not just a model
ReactLangChainRAGFastAPIPyTorch

Backend & Cloud Architecture

APIs and infrastructure that stay fast and cost-efficient at millions of users — microservices, queues, observability and multi-cloud, proven to 9M+ users and 250K+ daily actives.

For — products that need to scale without breaking
MicroservicesAWSGCPAzureKubernetesgRPC

Fractional AI Lead & Consulting

Senior AI architecture, technical direction, audits and team setup — without a full-time hire. We help you choose the right approach, de-risk it, prove it, and get your team shipping.

For — startups adding AI that need senior leadership, part-time
AI strategyArchitecture reviewModel selectionEvals & guardrailsTeam setup
03 Track record

We've shipped products people pay for

AI is the new part — designing, billing and scaling real SaaS is the part we've done for years. Multi-tenant apps, payments, real-time, dashboards, all the way to ARR.

SAAS

Products that bill & scale

Multi-tenant web apps and AI SaaS — subscriptions, payments, roles and dashboards, built to grow.

PAYMENTS

Real money, in production

Wallet & payment systems that processed 3cr+ in transactions — plus fintech full-stack at Jify.

REAL-TIME

Live at scale

Audio/video (WebRTC), 10M+ notifications a day and 250K+ daily actives — without falling over.

REVENUE

$0 → real ARR

Shipped a web service to $100K/month and a product to ~$1.2M ARR.

Shipped, not theorized
$100K/mo · ~$1.2M ARR · 9M+ users · 3cr+ in payments. We've already built and run SaaS that real users pay for — Buildgent is that experience, pointed at your product.
04 How we build

AI systems, built to survive production

Not a chatbot demo — engineered agentic systems with retrieval, evals, guardrails and a human in the loop where it counts.

01

Planner–executor agents

Agents that decompose a task, call tools through a typed registry (MCP), and recover from failure — orchestrated with LangGraph.

02

Retrieval & memory

Grounded in your data: embeddings + hybrid search over a vector store, reranking and citations — so answers are traceable, not hallucinated.

03

Evals & guardrails

Golden datasets, LLM-as-judge and rubric grading gate every change in CI; structured outputs, validation and prompt-injection defenses on the edges.

04

Human-in-the-loop

On consequential actions the system pauses for review, with full traces and audit trails — autonomy where it's safe, oversight where it matters.

Eval-driven delivery
Nothing ships below the bar. We treat AI quality like tests — golden datasets, LLM-as-judge + rubric grading, and regression gates in CI — so behaviour is measured, not vibes.
Trust & safety
Your data stays yours — we don't train on it.
PII redaction & field-level validation.
Human-in-the-loop on consequential actions.
Guardrails, citations & audit trails.
References from past teams available on request.
05 Founders

One builds the brain. One builds the whole product.

AG

Anshul Goel

Agentic AI · Document Intelligence · Backend Scale
Engineering Lead @ ReBillion
Bangalore, India

Builds the brain — autonomous AI systems that read messy real-world documents and act on them. Grew from intern to tech lead scaling platforms to millions before going deep on agentic AI.

  • ~80% client man-hours cut with agentic AI
  • Agentic control plane — LangGraph · Vertex AI · OCR/IDP
  • Scaled platforms to 9M+ users · 250K+ DAU
KS

Karan Singla

Senior AI Full Stack Developer
LangChain/LangGraph agents · RAG · ML (TF/PyTorch)
Senior Dev @ Labcorp
M.Eng, Concordia University, Montreal, Canada
Bangalore, India

Builds the whole product — LLM agents and RAG on top, React + Python/Go/Java underneath, ML models in the loop. 8+ years shipping AI products across two continents, with a Master's in Software Engineering.

  • GenAI in production — LangChain/LangGraph agents · RAG
  • 65% faster API responses · 50× data scale
  • Mentored 10+ engineers
06 The journey

Two paths. One studio.

We started at the same university and the same first company — then the road forked across two continents, and brought us back as co-founders.

2015 2026 ANSHUL KARAN
2015
Chitkara University
2018–19
Leher · early careers, together
2019–22
Scaled to 9M+ users · 250K+ DAU
2023→
Eng Lead, agentic AI @ ReBillion
2022–24
M.Eng, Concordia · Montreal
2023→
AI Full-Stack — AAVAA, now Labcorp
2026
Reunited — Buildgent
2015 · 2018–19

Shared start

Both at Chitkara University, then both at Leher — early careers side by side.

Anshul · India
2019–22

Scaled to 9M+

250K+ DAU, 100+ microservices.

2023→

ReBillion

Eng Lead, agentic AI.

Karan · Montreal
2022–24

M.Eng, Concordia

Moved to Canada.

2023→

AAVAA → Labcorp

Senior AI full-stack.

2026

Reunited — Buildgent

Two paths converge into one studio.

Built together from the start. Same university, same first company — we've been shipping side by side since before this studio existed.

07 Engagements

Proof, not promises

The systems we've led and shipped — the studio's track record, told in challenge, approach and result.

ReBillion · Agentic AI · Anshul-led

Autonomous AI for U.S. Real Estate

Agentic AI control plane · 2023–present
Challenge
Real-estate transactions buried in manual coordination — signatures, deadlines, legal PDFs nobody wants to read.
Approach
An agentic control plane (LangGraph + Vertex AI, tool-calling via MCP) over an OCR/IDP stack (Textract, Document AI, Mistral, LlamaParse) — with field-level validation, citations and human-in-the-loop on consequential actions.
Result
~80% fewer client man-hours — clients close more deals without scaling headcount.
80%
manual coordination removed
Multi-agent
LangGraph · Vertex AI
4 engines
OCR / IDP stack
Leher · Scale & Systems · Anshul-led

Scaling a Social Platform to Millions

Multi-cloud infrastructure · 2018–2023
Challenge
Build and scale audio-video social products for a fast-growing, massive audience — reliably and cheaply.
Approach
100+ microservices across AWS, GCP & Azure, a 10M+/day push system, on near-zero cloud cost via startup credits.
Result
9M+ users, 250K+ peak DAU, a $100K/month service and ~$1.2M ARR — on near-zero infra (startup credits).
9M+
users served
250K+
peak DAU
~$0
infra (credits)
AAVAA · AI Full-Stack · Karan-led

AI-Powered Full-Stack Platform

React + Python/Go · Montreal · 2023–2025
Challenge
Ship full-stack apps with real-time AI decisioning and conversational experiences — fast and production-grade.
Approach
High-performance REST & gRPC services, RAG and LLM agents (LangChain/LangGraph) plus ML (TF/PyTorch), on AWS + Kubernetes with CI/CD.
Result
65% faster API responses with real-time AI features running in production.
65%
faster API responses
Full-stack
React · Python · Go
RAG
agents in prod
08 Process

How we work

A clear, low-risk path — you see value before you commit to the whole build.

01

Frame

We find the real problem and the workflow behind it — not just the feature request.

02

Prototype

A working POC, fast — so we validate before anyone invests in the full build.

03

Harden

Eval-driven: golden datasets, LLM-as-judge, regression gates in CI, guardrails and edge-case coverage — the part most demos quietly skip.

04

Hand off

Docs and knowledge transfer, so your team fully owns what we built.

Typical engagement: focused 6–8 week sprints — boutique cadence, senior hands on every line.

09 Toolkit

One combined stack

Both founders' tech, merged into one studio toolkit across the whole pipeline. Tap a layer to see where we go deep.

Claude GPT-4-class Gemini Llama / Qwen Mistral Model routing LangGraph LangChain MCP Tool-calling Multi-agent Guardrails Embeddings pgvector Pinecone Hybrid search Reranking LLM-as-judge Golden datasets LangSmith / Langfuse Prompt versioning Token-cost Textract Document AI LlamaParse IDP TensorFlow PyTorch Scikit-learn React Angular TypeScript Python Go Java Node.js gRPC AWS GCP Azure Docker Kubernetes CI/CD
Showing all 43 tools across 9 layers AI & LLM Engineering
10 Why us

Why this studio

01

Two senior owners

No junior hand-offs, no account managers. You work directly with the people who build it.

02

End-to-end coverage

Idea → model → product → scale. Two complementary stacks that cover the whole pipeline, no gaps.

03

Production, not demos

Systems that run in production and cut real cost — proven at millions of users, not in a notebook.

04

Continuity & focus

Two people who can both own the work, taking on a deliberately limited number of projects at once.

What should we build together?

Tell us what you're shipping. The first 30-minute call is on us.