Assistants on your knowledge
Retrieval over your documents, systems and policies, answering in Malayalam and English with sources shown and handoff when it does not know.
At Infynix, we craft growth engines that inspire and endure.
We engineer autonomous AI agents, intelligent workflow pipelines, and custom LLM tools that streamline business operations and eliminate manual administrative work. Manual workflows, fragmented tools, and administrative bottlenecks burn hundreds of high-value team hours every month. Enterprise AI should not be generic chatbots; it must connect directly to internal company databases, CRM tools, and communication channels with deterministic guardrails.
OUR CAPABILITIES IN
AI Agents & Business Workflows
01
Autonomous Multi-Agent Systems
02
RAG & Knowledge Extraction
03
Workflow Automations & APIs
04
Enterprise Guardrails & Evaluation
How We Work
01
Measure
We audit your current systems, marketing, and funnels.
02
Design
We architect a custom strategy bridging your exact gaps.
03
Build
We deploy the tech, content, and pipelines required.
04
Launch
We seamlessly roll out the solution with zero downtime.
05
Re-Measure
We track every metric to ensure it meets our strict KPIs.
06
Scale
We optimize the winning channels to compound your returns.
AI product development and agents for Kerala startups and businesses building products where the intelligence is the product: assistants that answer from a company’s own knowledge, agents that complete tasks across systems, document and image understanding at volume, and the retrieval, evaluation and guardrails that turn a demo into something a customer can rely on. Built from Kochi for products sold in India and abroad.
Most AI products fail between the demo and the deployment. The demo works on ten examples; the product has to work on ten thousand, in Malayalam and English, on messy real-world inputs, within a cost budget, without inventing facts, and with an audit trail when it gets something wrong. The engineering that closes that gap is retrieval, evaluation, guardrails, observability and honest measurement, and it is most of the work.
We build AI-native products with that engineering from the start: the data pipeline, the retrieval layer over the company’s knowledge, the model choice and the hosting that fit the data protection requirements, the evaluation set that tells us when it is good enough, the guardrails that keep it inside what it knows, and the product around it that users actually adopt.
We work with startups in Infopark, Technopark and Cyberpark, with KSUM-backed companies, and with established businesses building AI features into their own products, in healthcare, education, legal, finance, logistics and customer service.
What we build
Agents that complete tasks
Multi-step workflows across your systems with tool use, approvals and a person in the loop where the cost of an error is high.
Document and image understanding
Extraction, classification and summarisation of forms, invoices, records and images at volume, with review queues.
Retrieval and data pipelines
Ingestion, chunking, indexing and freshness for the knowledge the product answers from.
Evaluation
Test sets, scoring and regression checks so quality is measured rather than assumed, release after release.
Guardrails and safety
Input and output checks, grounding requirements, refusal behaviour and audit logs.
Model choice and hosting
Off-the-shelf, fine-tuned or open models chosen for the task, the cost and where the data may legally go, hosted in Indian regions where required.
Product and interface
The screens, flows and integrations that make the intelligence usable, designed and built by the same team.
Typical engagements and timelines
| Engagement | Typical timeline |
|---|---|
| Feasibility and evaluation set | 2 to 3 weeks |
| Assistant or agent MVP | 6 to 10 weeks |
| Document understanding pipeline | 4 to 8 weeks |
| Production hardening | 4 to 8 weeks |
| Ongoing product team | Monthly |
Who this is for
AI startups
Founders with a working demo who need the engineering that makes it a product investors and customers can rely on.
Healthcare
Assistants and document tools for patient enquiries, records and administration, with clinical decisions left to clinicians.
Education
Tutoring, assessment and administration products in Malayalam and English.
Legal and finance
Document review, drafting and research products with the audit trail the professions require.
Customer service
Assistants that resolve routine enquiries on WhatsApp and web and route the rest with context.
Established businesses
Companies adding AI features to an existing product without rebuilding it.
How we engage
Feasibility first
A paid engagement that builds the evaluation set and proves whether the product can reach the quality bar.
Fixed-scope MVP
A defined product built, evaluated and launched with guardrails and review in place.
Product team
A named team shipping releases on your roadmap month to month.
From demo to product
The gap between a demo and a product is where AI startups run out of money. The demo answers ten questions well. The product has to answer ten thousand, on messy inputs, in two languages, without inventing facts, within a cost per query the business can afford, and with a record of what it said when a customer complains. Closing that gap is engineering: retrieval that finds the right context, evaluation that measures quality on a real test set, guardrails that keep the model inside what it knows, and observability that shows what happened.
We build that engineering from the start rather than after the first outage, and we measure quality on a test set drawn from real usage so the release decision is a number rather than an impression.
Real test sets
Drawn from real inputs, scored, and run on every release.
Retrieval done properly
The right context found, fresh, with sources.
Guardrails
Grounding, refusal and output checks that keep the product honest.
Observability
Every query, context and answer logged for debugging and audit.
Agents with a person where it matters
An agent that completes a task across systems, booking an appointment, raising a purchase order, updating a record, drafting and sending a reply, is valuable exactly in proportion to how much it can be trusted. Trust is built by design: clear tool boundaries, approvals where the cost of an error is high, a person in the loop until the numbers justify removing them, and an audit trail of every action taken.
We build agents with those controls, integrated with the systems they act on through their APIs, and we remove approvals one action type at a time as the evaluation earns it.
Tool boundaries
Exactly what the agent may do, and nothing else.
Approvals
A person confirms where an error would be expensive.
Graduated autonomy
Review removed only where the numbers justify it.
Audit trail
Every action recorded with its context.
Malayalam, cost and the model decision
Products for Kerala users have to work in Malayalam and Manglish as well as English, and models vary widely in how well they handle Indian languages. Cost per query decides whether a consumer product can exist at all. Data protection decides where the model may run. The model decision is therefore three decisions: quality on your languages and tasks, cost at your volume, and hosting that fits the DPDP Act and your customers’ contracts.
We evaluate candidate models on your own test set in your languages, cost them at your projected volume, and host in Indian regions or on your infrastructure where residency is required, and we revisit the decision as the field moves.
Evaluated in your languages
Malayalam, Manglish and English quality measured, not assumed.
Costed at volume
Cost per query modelled for the product to be viable.
Residency
Indian regions or your infrastructure where the data requires it.
Revisited
Model choice reviewed as better options arrive.
Products a customer can rely on
The product around the model matters as much as the model. Users need to know what the assistant can and cannot do, see where an answer came from, correct it easily, and reach a person without friction. Enterprise customers need admin controls, usage reporting, data handling documentation and the security evidence their procurement teams ask for. We design and build the product layer with the same team that builds the intelligence, so the two are not at odds.
The result is a product that ships with the documentation, controls and measurement a buyer expects, which for a Kerala startup selling to India or abroad is what gets it past due diligence.
Transparent to users
Capabilities, sources and corrections visible in the interface.
Enterprise controls
Admin, usage, data handling and security evidence built in.
Handoff to people
A person reachable when the product cannot help.
Due-diligence ready
Documentation that survives a procurement review.
AI Agents & Business Workflows questions
How do custom AI agents integrate with existing software?
We build direct API webhooks and database connectors into tools like HubSpot, Salesforce, Slack, WhatsApp, and PostgreSQL.
Is our company data kept secure and private?
Yes. All data pipelines operate in isolated enterprise cloud environments or on-premise infrastructure with zero retention by public foundation models.
We have a working demo. What comes next?
A feasibility engagement that builds a real evaluation set from real inputs, measures the demo against it, and proves whether the product can reach the quality bar at a viable cost. Then the engineering that closes the gap: retrieval, guardrails, observability and the product layer.
Can the product work in Malayalam?
Yes, and it has to be measured rather than assumed. Candidate models are evaluated on your own test set in Malayalam, Manglish and English, and the product is designed for the languages users actually type.
Where does the data go?
Where the DPDP Act and your customers’ contracts allow. Models are hosted in Indian cloud regions or on your infrastructure where residency is required, and what data goes where is documented.
Can you build agents that act in our systems?
Yes, with clear tool boundaries, approvals where an error would be expensive, a person in the loop until the numbers justify removing them, and an audit trail of every action.
How do you measure whether it is good enough?
On a test set drawn from real usage, scored and run on every release, with regression checks so a change that helps one case does not break another. The release decision is a number.
Do we need our own model?
Usually not. Off-the-shelf models with good retrieval and guardrails do most jobs. Fine-tuning or open models are used where quality on your languages, cost at your volume or data protection justify them.
Can you add AI features to an existing product?
Yes. Features are built into the existing product through its APIs and data, with the same evaluation and guardrails, without rebuilding what already works.
Who owns the product?
You do. Source, models, evaluation sets, infrastructure and documentation are handed over in your name, with the architecture documented for the team you will hire.
How long does it take to get to a first release?
A feasibility engagement takes two to three weeks, and an assistant or agent MVP with evaluation and guardrails in place is typically six to ten weeks after that.
