01
New projects
A paragraph about the problem is enough to start. We reply within two working days, usually with questions rather than a quote.
AI engineering Agents that do real work.
Retrieval over your own data, tool access through MCP, and an evaluation suite so you can prove the thing behaves before it ever reaches a customer.
Agents that do real work
Language models are the easy part. The work is everything around them: retrieval, permissions, measurement, and cost.
Agents & orchestration
Agents that hold state across steps, hand work to each other, and retry without duplicating side effects. Once there is more than one agent you are writing distributed systems again, so we treat it as one.
ClaudeMCPA2ATemporal
Retrieval over your own data
RAG that respects the permissions of the source system, cites what it used, and returns nothing rather than something plausible when the index has no answer.
pgvectorPostgresHybrid searchRerankers
Evaluations & observability
A trace per run, a regression suite built from your real historical inputs, and a cost ceiling that trips well before the invoice does. Nobody should ship an agent they cannot measure.
Eval suitesTracesOpenTelemetryFinOps
Integration into existing products
Model work inside the product you already have — your auth, your data boundaries, your release process. No separate AI island that nobody can operate.
TypeScriptPythonQueuesFeature flags
An agent with tools is an agent with blast radius
We design the permission boundary before the prompt, because the prompt is not a security control.
- 01
A permission boundary per tool
If the agent cannot call the tool, no instruction hidden in any document can make it call the tool. Scope is the control that holds; filtering the text is not.
- 02
Prompt-injection testing
Instruction-shaped text arrives in tickets, PDFs, web pages and commit messages. We test against it before the agent has anything worth attacking.
- 03
A human step where it matters
Anything that moves money, changes access, or sends mail on your behalf stops for a person. That is a product decision, and we make it explicitly.
- 04
An audit trail that exists first
Every tool call logged with the input that triggered it, retained long enough to answer the question you will eventually be asked.
AI services
Each one sets out what is included, how it is delivered, and the questions people ask before starting.
- AI agent developmentProduction AI agents built with scoped tool access, state that survives a retry, an evaluation suite from your real inputs, and a cost ceiling per run.
- RAG & retrieval systemsRetrieval-augmented generation that respects source permissions, cites what it used, and says so instead of guessing when the index has no answer.
- MCP server developmentModel Context Protocol servers that give AI agents scoped, audited access to your systems: OAuth scopes per tool, rate limits, and a log of every call.
- LLM evaluation & observabilityEvaluation suites built from your real inputs, a trace for every run, drift alerts and cost ceilings, so an AI feature is measured rather than assumed.
- AI & LLM security reviewAn adversarial review of AI agents and LLM features: prompt-injection testing, tool permission boundaries, data leakage paths, and a written report with fixes.
- Generative AI & chatbotsChatbots and generative AI features grounded in your own data, connected to your systems, measured with evaluations and kept inside a cost budget.
- AI consulting & strategyAI consulting from engineers: find the use cases worth building, check your data and systems are ready, decide build or buy, and leave with a costed roadmap.
- AI workflow automationAutomate the repetitive work between your systems with AI: triage, data entry, summaries and approvals, with people kept in the loop where it matters.
- Document processing (IDP)Extract data from invoices, contracts, forms and IDs with AI: OCR plus language models, checks against your rules, and human review when confidence is low.
- Voice AI agentsVoice AI agents that answer and make calls: speech recognition, natural replies, actions in your systems, handover to people, and compliance built in.
- Multi-agent systemsMulti-agent AI systems with clear roles, durable state, safe hand-offs and evaluation — built like the distributed systems they are.
- LLM fine-tuningFine-tune language models when prompting and RAG fall short: data preparation, training on open or hosted models, evaluation and deployment.
- AI integrationAdd AI to the software you already run — CRM, ERP, support desk or your own product — inside its permissions, data boundaries and release process.
- AI copilotsCustom AI copilots for sales, support, operations or your product: grounded in your data, able to take approved actions, and measured in real use.
06 / Let’s build
Bring us the difficult problem.
02
Careers
Engineers, designers and researchers who like systems that are hard to get right. Send something you built and what you would change about it now.
03
Studio
Based in India and working remotely, with overlap hours agreed up front so your team always has a time to reach us.
04
Read
Guides on AI agents, RAG, MCP, tokenization and chain abstraction, written by the engineers who build them.