We design and build AI agents and chatbots that answer customers, qualify leads, process documents and complete tasks inside your systems, with guardrails and a human in the loop where it matters.
1// An agent with tools, limits and a human fallback2export const leadQualifier = createAgent({3 instructions: 'Qualify the enquiry and book a call.',4 tools: [searchCatalogue, checkCalendar, createCrmLead],5 maxSteps: 6,6 guardrails: [noPricingPromises, piiRedaction],7 onUncertain: (ctx) => handOffToHuman(ctx),8});Worked with
Problems we solve
Many teams spend hours on work that follows clear patterns: answering the same questions, qualifying enquiries, reading documents and copying data between systems.
Leads arrive at night and on weekends, and the fastest competitor wins them.
Support and sales teams answer the same questions about products, policies and status every day.
Invoices, forms, contracts and applications are read and typed into systems one by one.
A chatbot demo looked impressive, but it made things up, couldn't use your data and was never trusted in production.
Our solution
We build agents that know your business, act through your systems, and know when to hand over to a person.
Agents answer from your approved content and data, cite their sources and say when they don't know.
Agents use tools: they look up orders, check calendars, create CRM leads and update records through secure APIs.
Guardrails, permission limits, personal-data protection, human review for sensitive actions and full logs of what the agent did.
AI agent development services
AI agent and chatbot development services for sales, support and operations.
Chatbots on your website, WhatsApp or app that answer questions from your knowledge base, check order or ticket status, and hand over to your team with full context when needed.
Agents that respond to enquiries instantly, ask qualifying questions, recommend the right product or service, and book calls or create CRM leads.
Agents that read invoices, purchase orders, forms and applications, extract the data, check it against rules and send it to your systems for review.
Assistants that let staff ask questions across your SOPs, policies, manuals and project documents, with answers that link to the source.
Agents that carry out multi-step tasks such as triaging requests, drafting replies, updating records and producing summaries, with approval steps where needed.
Add AI to your own SaaS or app: assistants, smart search, summaries and drafting, designed for accuracy, cost control and privacy.
What you get
We design every agent around a measurable job, not a demo.
Enquiries answered any time of day.
Repetitive work handled, people focus on judgement.
Grounded in your approved content, with sources.
Agents act in your CRM, calendar and systems.
Guardrails, limits and a full record of actions.
Clear rules on what data is used and stored.
Why us
We build agents as production software, with tests, logs and monitoring.
We build the CRMs, portals and APIs agents need to act on.
Human review, limits and data protection are part of every design.
A proposal with scope and a customised quotation before work starts.
A free strategy call, then a written proposal with a customised quotation.
Our approach
Open each step to see what it covers.
We check the task is a good fit for AI, define success and decide where people stay in control.
Approved knowledge sources, the systems the agent can use, and permission limits.
A working prototype tested against real examples, with accuracy measured, not guessed.
Safety rules, personal-data handling, hand-off rules and cost limits.
Internal use first, then customers, with monitoring of every conversation and action.
Regular reviews of logs and feedback to improve answers and actions.
Common AI problems
These are common reasons AI projects disappoint, and what we do differently.
Technology
Models and tools chosen for each use case's accuracy, cost and privacy needs.
Options
How the main options compare.
| Criteria | Custom AI agent | No-code chatbot builder | Rule-based bot |
|---|---|---|---|
| Understands free text | Yes | Yes, within limits | Only fixed options |
| Uses your systems | Through secure APIs | Limited integrations | Rarely |
| Control and guardrails | Designed for your rules | Platform settings | Fully scripted |
| Data handling | Your rules and hosting choices | Vendor's platform | Simple |
| Best for | Tasks that need your data and tools | Simple FAQs | Fixed menus and flows |
Not every task needs AI. If a simple automation or rule-based flow is better, we'll recommend it.
Use cases
Common AI agent use cases we design for.
Qualify property enquiries and book site visits.
See the workAnswer admission questions and guide students.
See the workRead POs, answer dealer queries and check stock.
See the workBook appointments and answer service questions.
In-product assistants, search and summaries.
See the workKnowledge assistants over SOPs and documents.
See the workHow we work together
Every engagement starts with a written proposal and a customised quotation.
A focused prototype for one task, evaluated on your real examples.
Best for: Testing whether AI fits
A fully integrated, monitored agent with guardrails.
Best for: Proven use cases
Continuous improvement, new agents and model updates.
Best for: Businesses scaling AI
Guide
An AI agent is software that uses a large language model to understand a request, decide what to do, and take actions through tools, such as searching a knowledge base, checking a calendar or updating a CRM, to complete a task.
A chatbot mainly answers questions. An agent can also act. Many business systems use both: a chat interface backed by agent capabilities.
Good first agents handle frequent, well-defined tasks where mistakes are easy to catch: answering common questions from approved content, qualifying enquiries, extracting data from documents, and drafting replies for review.
Avoid starting with tasks where an error is costly and hard to detect.
Ground answers in your own approved content using retrieval, ask the model to cite sources, and give it a clear way to say it doesn't know. Test against a set of real examples before and after every change.
Accuracy is measured, not assumed.
Decide what data the agent may see, minimise personal data sent to models, redact where possible, and set retention rules. Choose model providers and hosting that match your data obligations.
Tell users when they are talking to AI, and how their data is used.
Model costs depend on usage and model choice. Use smaller models for simple steps, cache repeated work, set limits per user or task, and monitor spending.
Measure the value of each agent against its running cost.
An agent becomes truly useful when it can do things, such as create a CRM lead, book an appointment or check an order. Each action should use a defined, limited tool with clear permissions, rather than open access to your systems.
For actions with real consequences, such as refunds or changes to orders, a person should approve before the agent proceeds.
Before launch, test the agent against a set of real questions and expected answers, including tricky and out-of-scope ones. Track accuracy, hand-off rates and failures, and repeat the tests whenever prompts, content or models change.
After launch, review real conversations regularly. They show where answers are wrong, where content is missing and where customers get stuck.
Customers in India often write in Hindi, Hinglish or regional languages, sometimes mixing scripts. Modern models handle many of these well, but test with real messages from your customers rather than assuming.
Decide which languages the agent should reply in, and keep approved content available in those languages where accuracy matters.
Start with one well-defined job, such as answering product questions or qualifying leads, and measure it. Once it works reliably, add more tools, channels and tasks.
This approach keeps risk low and shows clear value early, which makes it easier to decide where to invest next.
Costs depend on the number of tasks and channels, integrations with your systems, knowledge sources, guardrails and review flows, evaluation effort, model usage and ongoing support.
We don't publish fixed prices or packages. After a strategy call, we send a written proposal with a customised quotation.
Before you talk to us
A chatbot mainly answers questions. An AI agent can also take actions through tools, such as booking calls or updating records, to complete tasks.
Yes. Agents answer from your approved content and data, and use your systems through secure APIs.
We ground answers in approved sources, ask for citations, test against real examples and give the agent a clear path to say it doesn't know or hand over to a person.
Yes. Agents can work on WhatsApp through the WhatsApp Business API, as well as on your website and apps.
We minimise and protect personal data, set retention rules and choose model providers and hosting that fit your obligations.
Yes. Hand-off rules pass the conversation to your team with full context.
We choose per use case, including models from OpenAI and Anthropic and open-source models, based on accuracy, cost and privacy needs.
Yes. We add assistants, smart search, summaries and drafting to SaaS products and apps.
Yes, by connecting to your calendar or booking system with clear rules and limits.
Often, yes. We test with real customer messages in the languages you need before launch.
Yes. Conversations are logged so your team can review answers and improve the agent.
It depends on tasks, channels, integrations, guardrails and usage. We do not publish fixed prices; share your use case and we will send a customised quotation.
Contact us
Share a few details about the work and the systems involved. An engineer reviews every enquiry and gets back to you to set up a call.