Hokayantra

AI consulting and fullstack development

We point AI projects in the right direction, then build them to last.

An AI consulting and fullstack development company with 10+ years of engineering experience behind it, working worldwide. Most AI projects do not fail at the model. They fail at the retrieval that quietly went stale, or the evaluation nobody got round to building. That is the work we do.

Start a conversationSee our capabilities

Services

Where teams usually start

Four ways in. Most engagements begin with one and pick up others as the shape of the problem becomes clear.

01 / Capabilities

What we are engineered to do

Six practices that we run as one team. Most engagements draw on several of them at once, because production AI rarely fails in only one place.

  • Applied LLM systems

    Prompts are the easy part. The hard part is holding quality steady when the model version changes underneath you, and knowing what a request costs before finance asks.

  • Autonomous agents

    An agent that cannot explain why it did something is not shippable. We build the boring parts: permission boundaries, a human in the loop where it matters, and a trace for every decision.

  • Retrieval infrastructure

    Vector search on a demo corpus tells you almost nothing. Real systems live or die on chunking, hybrid ranking and whether the index reflects what your data looked like this morning.

  • Evaluation and reliability

    This is where most projects stall. Without a test set you trust, every release is a guess, and nobody can say whether last week's prompt change made things better or worse.

  • Data and platform engineering

    The model is a small piece of the bill. Underneath it sits ingestion, storage and serving, and those decide your latency and most of your cost.

  • AI product engineering

    Somebody has to build the interface, the permissions, the usage metering and the admin tools. We do that too, so the model arrives inside a product rather than next to one.

Diagnose, prototype, harden, transfer

02 / Approach

A method, not a pitch deck

  1. 01

    Diagnose

    We look at your data before we look at models. Usually the answer to what should we use is decided by what your data can actually support, and that conversation is cheaper to have in week one than in month four.

  2. 02

    Prototype

    One narrow slice, built against your real data. Demo corpora hide every problem worth finding, so we skip them.

  3. 03

    Harden

    Evals, guardrails, cost ceilings, tracing, a rollback that someone has actually tested. Unglamorous, and the reason a pilot becomes a product instead of a story about a pilot.

  4. 04

    Transfer

    We write the runbook and sit with your engineers until they can debug it without us. An AI system you cannot maintain is a liability with a subscription.

03 / Engagements

Three ways to work with us

Scope and commitment differ. The engineering standard does not.

  • 2 to 4 weeks

    Discovery sprint

    A fixed-scope engagement to establish whether an AI approach is viable, what it will cost to run and where the risk sits.

    • Technical feasibility assessment
    • Data and retrieval audit
    • Evaluation plan and success criteria
    • Costed delivery roadmap

  • 3 to 9 months

    Build partnership

    We take end-to-end responsibility for delivering a system into production, working alongside your team throughout.

    • Full system design and build
    • Evaluation and reliability engineering
    • Deployment and observability
    • Knowledge transfer at handover

  • Ongoing

    Embedded team

    Senior engineers working inside your organisation to raise delivery throughput and establish AI engineering practice.

    • Senior engineers in your workflow
    • Architecture and technical review
    • Practice and standards development
    • Mentoring for your engineers

04 / Stack

Under the hood

We are not tied to a vendor. We pick per problem and we tell you why.

Models
Anthropic Claude, OpenAI, Google Gemini, open-weight models
Orchestration
Model Context Protocol, LangGraph, purpose-built event flows
Retrieval
pgvector, Qdrant, Elasticsearch, hybrid and reranked search
Evaluation
Custom eval harnesses, LLM-as-judge panels, regression suites
Infrastructure
AWS, Google Cloud, Kubernetes, Vercel, Postgres
Languages
Python, TypeScript, Go, SQL

  • Fine-tuning is usually the wrong first move

    It gets suggested early because it sounds like the serious option. Nine times out of ten better retrieval and a tighter prompt get you further, for a fraction of the cost, and without freezing you to a model version.

  • If you cannot measure it, you are guessing

    Teams tell us their accuracy improved. Ask how they know and it turns out someone tried fifteen queries by hand. Build the eval set first, even a small one, or you will argue about vibes for a year.

  • You should be able to fire us

    We write things down and we teach as we go. If leaving us would break your system, we did the job badly.

05 / Questions

Frequently asked

What does Hokayantra Technologies do?
Hokayantra Technologies is an AI consulting and fullstack development company, staffed by engineers with more than a decade of experience building and running production software. We build systems on large language models, agents and retrieval, and we stay on the hook for how they behave in production rather than in a demo.
How is an AI consulting firm different from an AI agency?
Mostly in what happens after launch. An agency is generally finished when the thing works on a laptop. An AI consulting firm is judged on the parts that only show up later, like cost per request at real volume, quality drift when a model is deprecated, and whether anyone can debug it at 3am.
What AI consulting services do you offer?
Six practices, and most projects touch several: applied LLM systems, autonomous agents, retrieval infrastructure, evaluation and reliability, data and platform engineering, and AI product engineering. Production AI rarely breaks in only one place, which is why we do not sell them separately.
Do you provide AI integration services?
Yes, and it is a good part of the work. AI integration services connect a model to the systems you already run: your data pipelines, your auth and permissions, your serving infrastructure, your logging. The model is the easy hire. Making it behave inside an existing codebase is the job.
Can I hire AI developers through you?
Yes. Our embedded team model puts senior AI developers inside your organisation, committing to your repo and sitting in your architecture reviews. Teams usually come to this after a pilot worked and they realised nobody in-house had time to productionise it.
How do engagements work?
Three shapes. A discovery sprint runs two to four weeks and answers whether this is worth building at all. A build partnership runs three to nine months and puts a system in production. An embedded team is ongoing. If a discovery sprint concludes you should not build the thing, that is a successful outcome and we will say so.
Which AI models and technologies do you use?
We are not tied to a vendor and pick per problem, because the sensible answer changes every few months. Currently that means Anthropic Claude, OpenAI and Google Gemini alongside open-weight models, orchestration via the Model Context Protocol or LangGraph, retrieval on pgvector, Qdrant or Elasticsearch, and delivery on AWS, Google Cloud or Kubernetes.
Who do you work with and how do we start?
We work remotely with clients worldwide, across North America, Europe, the Middle East and Asia. The fastest way to start is an email to [email protected] describing what you are trying to build and what is currently in your way.

Let us talk about what you are building.

Tell us the problem and the constraints. We will tell you honestly whether AI is the right tool for it.

Book a conversation[email protected]