We work across the full stack of modern software: from AI integrations bolted into existing platforms, to autonomous agents that do real work, to back-office automations and full product builds. One team, one bar, no hand-offs.
LLM apps, retrieval, fine-tuning, and AI features bolted into your existing product, without breaking what works.
We design and ship production-grade AI features into existing software. From RAG pipelines on your private data, to fine-tuned domain models, to embedded chat surfaces and inference cost engineering, we treat AI like the rest of your stack: testable, observable, on-budget.
Chat, search, summarization, classification, and content workflows on top of OpenAI (GPT-5, GPT-4o), Anthropic Claude (Sonnet, Opus), Gemini, Llama, Mistral, or self-hosted models. Built with LangChain or the OpenAI / Anthropic SDKs directly when overhead is the enemy.
Vector pipelines on your private data using Postgres + pgvector, Pinecone, Weaviate, or Qdrant. Hybrid BM25 + dense retrieval, cross-encoder reranking, grounded citations, eval harnesses you can run in CI.
Domain-specific small models, LoRA/QLoRA, distillation, RLAIF. Eval harnesses with LangSmith, Braintrust, or hand-rolled pytest suites. Promptfoo for prompt diffing.
OCR, image understanding, document AI, and voice using GPT-4 Vision, Claude Vision, Gemini, or fine-tuned ViTs. Production-deployed on AWS Bedrock, Vertex AI, or self-hosted vLLM.
Autonomous and multi-agent systems that take real work off your team: ops, support, research, sales, internal tools.
Agents are software that decides. We build agentic systems with LangGraph, OpenAI Agents SDK, or Claude with MCP that do real work: reading inboxes, drafting outreach, triaging tickets, running research loops, or orchestrating multi-step backend workflows. We start with one job-to-be-done, wire in real tools, and bake in evals so the agent gets measurably better over time.
One agent, one clear job: research, outreach, triage, QA. Built with LangGraph or OpenAI Agents SDK, tool-using, evaluated, deployable on day one.
Planner / worker / critic architectures using LangGraph state machines, CrewAI, or AutoGen. We design supervisor / sub-agent topologies that converge instead of looping forever.
Anthropic Claude with the Model Context Protocol (MCP) for clean tool access: CRMs, ticketing, Slack, calendars, internal APIs. Includes OpenClaw-style multi-agent orchestration when several Claude sessions need to cowork on the same task.
Trace every step with LangSmith, Langfuse, or OpenTelemetry. Score every run, ship improvements with confidence.
Workflow, data, and back-office automations that replace spreadsheets and manual ops with reliable pipelines.
Not every automation needs AI. Sometimes it just needs a properly built workflow. We design back-office automations end-to-end: data ingestion, ETL, integrations, scheduled jobs, idempotent retries, and dashboards your team will actually look at. Reliable, monitored, owned.
From quick wins with n8n, Make, or Zapier to durable Temporal, Airflow, or Prefect workflows when the scale demands it. We pick the lightest tool that survives production.
ETL/ELT with Airbyte, Fivetran, or Meltano. Transformations with dbt. Warehouses on Snowflake, BigQuery, or Postgres. Reverse-ETL via Hightouch or Census.
Connecting the 20 tools your business actually runs on. CRMs (HubSpot, Salesforce), billing (Stripe), support (Zendesk, Intercom), ops, with idempotent retries and observability baked in.
Dashboards, admin panels, and ops UIs using Django admin, Retool, Tooljet, or custom React. Replace spreadsheets with software your team trusts.
Web, mobile, backend, infra: production-grade product teams on demand. From zero-to-one to scale.
When you need a real engineering team (designers, frontend, backend, infra, QA) embedded on your problem for a quarter or two, we drop in as one. We ship product. From founding-team MVPs to platform rebuilds to last-mile launches, we work the way modern product teams do: weekly demos, two-week cycles, real users in week one.
React, Next.js, Django, FastAPI, React Native, Swift, Kotlin, picked for the job, not the resume. SSR or SPA depending on what the product actually needs.
Backend services in Python (Django, FastAPI) or TypeScript (Node, tRPC). GraphQL with Apollo, REST with OpenAPI specs, gRPC when latency matters. Postgres or DynamoDB as the system of record.
AWS, GCP, Vercel, Fly, Railway, Cloudflare, Kubernetes. Terraform / Pulumi for IaC. GitHub Actions for CI. Docker, observability with Datadog or Grafana / Loki.
Weekly demos, two-week cycles, design + engineering in the same room. Built with Claude Code coworkers for the boring bits, humans for the calls that matter.
Two weeks. Real users, real constraints, real numbers.
We start with a tight discovery loop, talking to your team, your users, and your data. We come back with a one-page brief: what we are building, what we are explicitly not building, the technical bets, and the timeline.
Working software in your hands, fast.
We build the riskiest 20% first, the part that proves the whole thing works. You see real product in three to four weeks, on a staging URL you can poke.
Two-week cycles, weekly demos, no surprises.
Production engineering: design, frontend, backend, infra, QA. Every Friday you see a demo. Every two weeks you decide what ships next. Nothing is ever opaque.
Launch, monitor, iterate, hand-off.
We go live, instrument everything, watch the first users, and stay close. When the platform is stable, we either retainer for ongoing work or do a clean handoff to your in-house team.
Scoped, priced, dated.
A defined outcome with a fixed scope and a fixed date. You know exactly what you are getting and when.
A dedicated pod, every month.
A dedicated team (designer plus engineers) embedded for a quarter or longer. Predictable monthly cadence, flexible scope.
Experienced engineers, your roadmap.
One to three experienced engineers join your team for the duration. Daily standups with your eng manager, weekly with us.
Tools we use in production, not just in demos. We pick the lightest thing that survives a real customer, then keep it observable and replaceable.
Working with something we haven't listed? Tell us. New tools earn their place when the work demands it. Start a conversation →
Most engagements kick off within two weeks of a signed scope. We sometimes have a same-week slot for a discovery sprint.
Standard practice. We can also sign yours.
Our HQ is in Indore, India. The team is distributed and we work with clients across continents (India, Middle East, UAE, Europe, Asia, the Americas). Lead engineers overlap your working hours, whichever timezone you are in.
You do. Always. We deliver clean repositories with documentation, CI, and a runbook you can hand to anyone.
Yes. We slot in. Most of our retainers and staff-aug engagements work alongside in-house teams. We adapt to your process, not the other way around.
Two-week cycles mean we can absorb change cleanly. Bigger scope changes become a re-scoped phase. No surprise invoices.
Retainer support is standard for 60-90 days after launch, then optional ongoing. We do not lock you in.
LangGraph for stateful multi-agent graphs, OpenAI Agents SDK for single-agent tool-use loops, and Anthropic Claude with the Model Context Protocol (MCP) for cleanly typed tool layers. We use CrewAI and AutoGen where they fit. For multi-Claude orchestration we use OpenClaw-style coworker setups. Observability via LangSmith or Langfuse.
Yes. We pick the lightest tool that survives production: n8n or Make for low-code workflows you want non-engineers to maintain, Temporal or Prefect for code-defined durable workflows at scale, and Airflow for data-heavy DAGs.
We use Claude Code as a coworker on most internal builds. It is part of how we ship product fast without dropping quality. For client work we can also set up Claude coworkers inside your own org so your team gets the same leverage after we hand the build over.
Backend: Python (Django, FastAPI), TypeScript (Node, tRPC). Frontend: React, Next.js, React Native, Swift, Kotlin. Data: Postgres + pgvector, Snowflake, BigQuery, dbt, Airbyte. AI: OpenAI, Anthropic Claude, Gemini, Llama, Mistral, with LangChain, LangGraph, or direct SDKs. Cloud: AWS, GCP, Vercel, Fly, Railway, Cloudflare, Kubernetes.
