AI Solutions

Production AI is not a chatbot demo.

Anyone can wire a model to a text box in an afternoon. The gap between that and software your business can rely on is retrieval over your actual data, measured accuracy, monitoring, and a human in the loop where mistakes are expensive. That gap is what we build.

Discuss your use case See what we build

Grounded, not guessed — answers cite the documents they came from.
Measured, not vibes — an eval suite runs before every change ships.
Watched, not launched-and-forgotten — cost, latency, and failure monitoring.
Humans in the loop — the model escalates when it should not decide alone.

anekant / support_agent.py

import anthropic

client = anthropic.Anthropic()

# retrieve, then answer — never answer blind
docs = index.search(query, top_k=5)

response = client.messages.create(
    model="claude-sonnet-5",
    system=SUPPORT_POLICY,
    tools=[lookup_order, create_ticket],
    messages=[{
        "role": "user",
        "content": with_citations(query, docs),
    }],
)

if response.confidence < THRESHOLD:
    escalate_to_human(response)  # not optional

What we build

Six AI systems we take to production

Each one starts from your data and your workflow — not from a template.

[01]

RAG & Knowledge Assistants

Chat with your documents, wikis, and databases. Retrieval-augmented generation that grounds every answer in your sources — with citations, so your team can verify instead of trust.

retrievalcitationsvector search

[02]

AI Agents & Tool Use

Agents that do things, not just say things — calling your APIs, updating your CRM, checking calendars, and drafting actions for approval before anything irreversible happens.

tool useCRMworkflows

[03]

WhatsApp AI Assistants

Customer support and lead qualification on the WhatsApp Business API. The assistant handles routine questions and captures leads; your team takes over the conversations that matter.

WhatsApp Business APIsupportleads

[04]

Document & Data Extraction

Invoices, tenders, forms, and scanned PDFs turned into structured, validated data. Field-level confidence scores route uncertain extractions to a person instead of into your database.

invoicestendersstructured output

[05]

Evaluation & Guardrails

Eval suites built from your real cases, red-teaming for prompt injection and misuse, fallback behavior when the model is unsure, and hard limits on cost per request.

evalsred-teamingcost control

[06]

AI Readiness Advisory

A short, honest engagement to decide what to automate first, what to buy off the shelf, and what to build — including the cases where the right answer is "not AI, not yet".

roadmapbuild vs buyprioritization

How an AI project runs

From data audit to monitored production

STEP 01Discovery & data auditWe map the workflow, inspect the data you actually have, and define what a correct answer looks like — in writing.

STEP 02Prototype with your real dataA working slice on your documents and cases within weeks — so you judge it on your problem, not a canned demo.

STEP 03Eval & hardenWe build an eval set from real cases, measure accuracy, fix the failure modes, and add guardrails and fallbacks.

STEP 04Deploy & monitorShip to production with cost, latency, and quality dashboards — and iterate on what the logs actually show.

Straight answers

The questions you should be asking

Will it hallucinate?

Sometimes, yes — any vendor who says otherwise is selling you something. What we can do is make hallucination rare, detectable, and low-consequence: ground answers in retrieved documents with citations, have the model say "I don't know" when retrieval comes back empty, measure the error rate on an eval set built from your real questions, and route high-stakes outputs through a human before they act on anything.

The right question isn't "will it ever be wrong?" — it's "is it wrong less often than the current process, and do we catch it when it is?"

What does it cost to run?

It depends on volume and model choice, and we'll estimate it for your workload before we build. Model APIs are metered per token, so a typical support or extraction request costs a fraction of a rupee to a few rupees. The bigger levers are engineering: using smaller models for routine steps, caching repeated context, and keeping prompts lean. We set per-request cost budgets and alerting so the bill can't surprise you.

For most workflows, the monthly API bill ends up small next to the staff hours it replaces — and we'll show you that math up front, not after launch.

Do we need our own model?

Almost certainly not. Training a model from scratch costs millions and fine-tuning is rarely the first tool to reach for. Hosted frontier models plus good retrieval and prompting cover the large majority of business use cases — and they improve every few months without you retraining anything.

Fine-tuning or self-hosted open-weight models make sense in narrower cases: strict data-residency requirements, very high volumes where per-token cost dominates, or a task format the base models genuinely can't handle. If you're in one of those cases, we'll tell you — and if you're not, we won't build you an expensive one anyway.

What about our data privacy?

Major model providers (Anthropic, OpenAI, Google) do not train on API data by default under their standard commercial terms, and we architect on that basis. Beyond the provider's terms: we send the model only what a request needs, redact or mask sensitive fields (names, phone numbers, financials) where the task allows, encrypt data in transit and at rest, and keep your document stores in your own infrastructure or region where required.

If your compliance requirements rule out external APIs entirely, self-hosted open-weight models are an option — with real trade-offs in capability and operational cost that we'll lay out honestly before you commit.

Next step

Have a workflow AI should be handling?

Tell us about the process and the data behind it. We'll reply within a day with an honest read on whether AI helps, what it would cost, and how we'd prove it works before you commit.

Start the conversation