Your team is doing work a machine should be doing.
We build AI agents that handle the repetitive work inside your business: support triage, invoices, data entry, follow-ups, reports. First process live in 30 days. Starts with a $750 audit that tells you what is worth automating, and what to leave alone.
You own everything we build.
Where our automation work is already live
| Product | What it took off people | Links to |
|---|---|---|
| PM Fund Manager | Reporting that took 2 to 3 days now runs in minutes | No public case study yet |
| Pray the Bible admin | Content publishing 65% faster, training cut from 2 weeks to 2 days | Case study |
| Tata Power SolaRoof | KYC verification from days to minutes, 99% asset tracking accuracy | Case study |
| Hindustan Loan | Loan approval from 7 to 10 days to 8 to 12 minutes | Case study |
| Companion App | Field process friction down 70%, scaled 50 to 500+ workers | No public case study yet |
Nobody owns the data. Every copy is a chance to get it wrong.
The cost is invisible because it is spread across people.
Your best people are doing your cheapest work.
Almost certainly true, and it is the most common outcome in this category.
Correct, and that is a design requirement, not an objection.
Built by the person you will actually talk to
I am Anand Yadav, founder of SightInfusion. I have been building software for over 7 years and I have personally delivered more than 52 products.
Automation is the part of this business where it is easiest to sell somebody something they do not need. So here is how I work: when you send an enquiry, I read it. In the audit, I am the one mapping your process. And if your process follows clear rules, I will tell you to buy the cheaper rules-based version, or a $20-a-month tool, instead of an AI agent, because an agent that makes a judgement call where a rule would do is how automation projects get expensive and unpredictable.
I have talked clients out of AI more than once. That is the job.
Most agencies describe the work. Here is what is yours at the end of it.
| Deliverable | What it is |
|---|---|
| Process map | Your process as it actually runs today: every step, every exception, every person, agreed before anything is built |
| The success number | What we are trying to move, measured before we start, so the result is a number and not a feeling |
| The agent | Built, tested against your real cases, running |
| Source code | Full repository, full IP transfer, in your account |
| The prompts and the rules | Not a black box. You get the instructions the agent runs on, in readable form |
| Runbook | What it does, what it will not do, how to stop it, who to call |
| Audit log access | Every action it took and why, from day one |
| Monthly cost model | What it costs to run, worked out on your real volume. See below |
| Handover walkthrough | A recorded session taking your team through the whole thing |
If any of these is missing at handover, the job is not finished. That is our standard, not an upsell.
You own it. Take it anywhere. The code, the prompts and the process map are yours on final payment. Run it in-house, hand it to another agency, or switch it off.
Most companies know they should be doing something with AI. Very few have been told which thing, or whether it will pay for itself. This is the short paid engagement that answers it.
| Area | What we look at |
|---|---|
| Your operations | The repetitive work costing you hours every week, measured in hours and wage cost |
| Your data | Whether you have what an agent would need, and what shape it is in |
| Your systems | Whether an agent can reach your CRM, inbox, accounting and spreadsheets without a rebuild |
| Your constraints | Privacy, GDPR, what your industry allows, and whether any of it is high-risk under the AI Act. See below |
| Your alternatives | Whether a rule, a $20 tool or a better spreadsheet does the job instead |
Why it is paid. A free assessment exists to sell you the biggest project the agency can build. This one is paid so that “do nothing yet” is an answer we are allowed to give you, and we do give it.
Fixed price, fixed length, credited back. $750. Two weeks. If you build with us afterwards, the full $750 comes off the first invoice. If you don’t, the report is still yours: build it in-house, or take it to another agency.
Three different things get sold as AI and they cost very different amounts.
| When it is right | What it costs to be wrong | |
|---|---|---|
| A rule | The logic is clear and written down | Almost nothing. It fails loudly and predictably |
| An agent | The input is messy, unstructured, or does not fit a rule | It can be wrong quietly. That is the whole risk |
| A chatbot | You want a question answered, not work done | Your customer gets a confident wrong answer |
An AI agent reads the information, decides what needs to happen, and then does it: updates the record, drafts the reply, files the document, raises the order. It works on its own and it comes back to a human when something needs approval.
We start every conversation by trying to make your problem a rule. Rules are cheaper, faster and give the same answer every time. We only reach for an agent when the input is genuinely messy: free text, scanned documents, exceptions that do not fit. If your process is rule-shaped, go to Custom Business Automation and pay us less.
Incoming messages read, sorted, drafted or routed to the right person. A human sends anything that goes to a customer, until you say otherwise.
Documents read, data extracted, records updated. No manual typing. Anything the agent is unsure about goes in a review queue, not into your accounts.
New enquiries scored, enriched, and followed up without anyone remembering to.
The weekly and monthly reports somebody currently rebuilds by hand, assembled and delivered on schedule.
Information moved between your CRM, accounting, sheets, inbox and internal database, and kept in sync. The re-keying problem, solved once.
Requests routed to the right approver, chased, logged and closed out.
| Where it lives | Who sees it | Why clients pick it |
|---|---|---|
| Agents your team uses | Nobody outside the company | The saving is immediate and easy to measure. Most clients start here |
| Agents your customers use | Your customers | Higher upside, higher risk. A wrong answer reaches a customer |
For the in-app version customers see directly, see AI Feature Integration & Assistants.
Where agents fit
The useful ones do a whole job end to end: read the inbox, pull the record, draft the reply, wait for a yes. That is why the sections below are about control and running cost rather than about models.
The number one reason these projects die is not that the agent is wrong. It is that it is wrong quietly, and nobody finds out until a customer does. So we design for that first.
| Control | What it means in practice |
|---|---|
| Approval steps on anything that matters | The agent proposes, a named human confirms. You choose which actions need it |
| A full audit log from day one | Every action, with the input it saw and the reason it gave. Searchable by your team, not just by us |
| It shows its reasoning | Written in plain language, not model internals |
| A review queue for not sure | Low confidence goes to a person instead of into your data. An agent that says it does not know is working correctly |
| Hard limits | A written list of what it may touch and what it may never touch. It cannot exceed it |
| A stop switch you control | Not a support ticket to us. A switch your team can press |
| Alerts when behaviour drifts | You hear from the monitoring, not from a customer |
And there is a legal reason, not just a practical one. Under GDPR Article 22, a person has the right not to be subject to a decision based solely on automated processing where it significantly affects them, and where such a decision is allowed, Article 22(3) requires real human intervention, the right to state their view, and the right to contest it. This has been enforceable since 2018 and it has nothing to do with the AI Act. If your agent declines a refund, screens a CV or flags an account, the human in the loop has to be a real decision, not a rubber stamp. We design it that way by default.
An agent has two costs: building it once, and running it every month. Every agency in this category will quote you the first. We have not found one that publishes the second, on any service page.
So here is ours, with the vendors’ own prices. Model prices, per 1 million tokens, checked 12 August 2026.
| Model | Input | Output | Source |
|---|---|---|---|
| Claude Haiku 4.5 | $1 | $5 | Anthropic pricing |
| Claude Sonnet 5 | $2 | $10 | Anthropic pricing |
| Claude Opus 5 | $5 | $25 | Anthropic pricing |
| OpenAI gpt-5.6-terra | $1 | $6 | OpenAI pricing |
| OpenAI gpt-5.6-sol | $2.50 | $15 | OpenAI pricing |
| Google Gemini 3.6 Flash | $1.50 | $7.50 | Google pricing |
| Google Gemini 3.5 Flash-Lite | $0.30 | $2.50 | Google pricing |
Batch processing is 50% off with Anthropic and Google. Reading from cache costs a fraction of fresh input, with Anthropic, cache reads are charged at 0.1x the input rate.
Our assumption, stated openly because it is an estimate and not a measurement: roughly 3 model calls per ticket, about 25,000 input tokens and 2,000 output tokens per ticket in total. That is 50M input and 4M output tokens a month.
| Model | No caching | With about 80% cache hits |
|---|---|---|
| gpt-5.6-terra | ~$74 | ~$38 |
| Gemini 3.6 Flash | ~$105 | Not published |
| Claude Sonnet 5 | ~$140 | ~$68 |
| Claude Opus 5 | ~$350 | Not published |
Now the part that costs us money to tell you: the tokens are not the expensive bit. Forty to three hundred and fifty dollars a month to handle two thousand tickets is a rounding error next to the integration work, the testing, and the human time spent checking the agent. Any agency selling you an agent on the promise of cheap tokens, or on how much model spend they will save you, is selling you the wrong thing, and so is any agency that will not tell you the running cost at all.
What we do about it: every proposal carries a monthly cost model built on your real volume, a spending cap on the model account, and an alert before the cap. And note this, because it is true of every cloud platform: spending caps are usually alerts, not brakes. Somebody still has to watch the number.
We are going to be straight about this, because the cost model above asks you to trust our numbers.
We have built internal systems that took large amounts of manual work off people, with published results:
Admin time down 60%, errors down 95%, reporting from 2 to 3 days to minutes, scaled from 100 to 10,000+ parties.
Publication time down 65%, 3x faster publishing, staff training from 2 weeks to 2 days, monthly active users up 40% in 3 months.
Case studyLead to order 45% faster, 99% asset tracking accuracy, KYC from days to minutes, on top of the client's existing Salesforce.
Case studyLoan approval rate from 35% to 89%, decision time from 7 to 10 days to 8 to 12 minutes, across 500,000+ applications in the first year.
Case studyWhat we cannot prove yet. Every one of those is a software build that removed manual work, not an AI agent deployment. We do not yet have a published before and after for an agent: this task took 14 hours a week, now it takes 40 minutes, and here is the wage cost either side. We are not going to invent one. When we have it, it goes here, with the baseline and the method.
And nobody else has one either. We went looking. Across the ranking pages in this category, the proof is developer headcount, project counts and years in business. One agency in the wider automation market publishes named-client percentages with no baseline hours; another’s hours-saved counter renders as an empty placeholder. Not one page we studied shows a real before and after with hours and cost. If you find one that does, it is worth more than anything on this page.
We connect to the tools you already have: CRM, ERP, accounting software, email, WhatsApp, Slack, spreadsheets and internal databases. Nothing gets replaced and nobody has to change how they work.
That matters more than it sounds. The most-cited explanation for why generative AI pilots produce nothing is not the model, it is that the thing was never wired properly into how the work actually happens.
Tata Power SolaRoofruns on top of the client’s existing Salesforce CRM. We added role-based access for channel partners and retailers, barcode-scanned asset installation, GPS-tagged inspections, KYC verification and offline mode, with real-time sync back into Salesforce, without replacing anything the client already ran. Lead to order time dropped 45% and asset tracking accuracy reached 99%.
See the case studyOrdered by how much real proof we have.
| Industry | What we automate | Proof we can name |
|---|---|---|
| CRM, HR & Internal Business Tools | Approvals, reporting, data entry between systems, onboarding | PM Fund Manager · FMSoft CRM |
| Fintech & Banking | Document and KYC processing, application handling, reconciliation | Hindustan Loan · Quickchain |
| Legal-Tech | Document intake, case data extraction, filing workflows | ActivePass · DigitsLaw · Gavel Auctions |
| Energy & Utilities | Field reporting, asset tracking, inspection workflows | Tata Power SolaRoof |
| Retail, POS & E-commerce | Order and invoice processing, stock alerts, supplier admin | Vencru · Vencru POS |
| Logistics & Field Operations | Dispatch, job assignment, field data capture | Tata Power SolaRoof (field module) · Companion App |
| Events, Media & Community | Content publishing workflows, moderation queues | Pray the Bible admin · Azad Sandesh |
| Healthcare & Wellness | Patient admin, records handling | No claim made |
| Real Estate & PropTech | Listing admin, enquiry handling, tenant workflows | No claim made |
| EdTech & Training | Enrolment, assessment admin, learner follow-up | No claim made |
Fit call
You tell us the task. We tell you honestly whether it is an agent, a rule, an off-the-shelf tool, or not worth doing. No charge, no report.
AI Readiness Audit
Only if you are not sure what to automate first, or a previous attempt failed. See above. If you already know the process, skip this.
Process map and baseline
We sit with the people doing the work. We measure the current cost before we change anything.
Build and test on your real cases
The agent runs against work you already know the answer to, so you can see how it would have behaved.
Shadow mode
Live alongside your team. It proposes, a human confirms. We tune it on the disagreements.
Go live with the controls on
Approval steps, audit log, limits, stop switch, alerts. Then we measure the same number again.
Monthly run and tune
Accuracy monitoring, tuning as your process and the models change, and a monthly report of what it handled and what it got wrong.
Great work Anand on building this app from scratch. Looking forward to more projects with you.
Anand did a great job in flutter development. He is well versed in several coding languages and provided the solution I needed. Is very good at communicating as well.
I recently collaborated with Anand and his team on creating my finance mobile application and I was thoroughly impressed. Their technical expertise ensured seamless updates, and their proactive communication kept me informed throughout the process. They demonstrated a strong commitment to quality, promptly addressing any issues that arose.
What these prove and what they don’t. They are 54 reviews and a 100% Job Success Score on a platform we do not control, which tells you we finish what we start. They do not tell you we have a track record of AI agents in production. Nobody in this market has published one either.
Claude (Anthropic), GPT (OpenAI), Gemini (Google), and open-weight models where the data cannot leave your infrastructure
Tool-calling agents, retrieval (RAG), scheduled and event-driven runs
REST and GraphQL APIs, webhooks, CRM and ERP APIs, email and WhatsApp, Google Sheets, ODBC
Where a rule is the right answer, see Custom Business Automation
Audit logging, accuracy tracking, cost and rate alerting
For finance, health and legal work where data must not leave your systems
We recommend the model and the stack that fit your process and your budget, not the one we prefer to write. Model choice is a cost decision as much as a quality one.
A chatbot answers questions. An agent does the work: it reads the situation, decides, then updates the record, drafts the reply, files the document. See the three-way comparison, including when a plain rule is the better answer.
That is the design starting point, not an afterthought. Anything that matters goes through a human approval step, low confidence goes to a review queue instead of into your data, every action is logged with its reasoning, and you have a stop switch your own team controls. All seven controls.
Most pilots fail. The two most common causes are that nothing was measured before the work started, and that the thing was never properly wired into how the job actually gets done. That is why days 1 to 5 are a process map and a baseline, and why the agent is tested against your real historical cases before it goes anywhere near live work.
On the model side, less than most people expect: roughly $40 to $350 a month for an agent handling 2,000 support tickets, depending on the model and caching. The vendors’ own published prices, dated, and the assumptions behind that estimate. Every proposal we send carries a cost model built on your real volume, plus a spending cap.
No. We use business API access where your content is not used for training, and where data must not leave your systems at all we use self-hosted open-weight models or on-device processing. Which route is right is part of the audit, because it changes the cost.
No. We connect to what you already run. See below.
Then that is what the report says, and it happens. That answer is worth the $750 on its own: it is the reason the audit is paid rather than free.
No. The report is yours either way. The $750 comes off the first invoice only if you do continue.
You do. Full source code, the prompts and the process map transfer to you on final payment, on accounts in your name. You can take it to another agency or run it in-house.
Usually, with conditions. GDPR Article 22 restricts decisions made solely by automation about people, and the EU AI Act treats HR, credit and essential-services use as high-risk even when it is purely internal. See how we design for it. This is not legal advice, but it will tell you which questions to take to a lawyer.
In practice teams handle more volume rather than shrink, because the work that goes away is the work nobody wanted. We are not going to promise you a headcount reduction we cannot measure, and if that is the only reason for the project, the audit will probably tell you the payback is thinner than you hope.
30 days from the start of the build. The week-by-week.
Fair question. Gartner reckoned that of thousands of vendors calling themselves agentic, only about 130 genuinely were. Ask us three things: show me an agent taking an action in a real system, not answering a question; show me the audit log; show me the prompts and rules it runs on. We give all three before you commit anything. Ask every other agency the same.
Then the agent changes with it. You have the prompts and the rules in readable form, so your own team can adjust the simple things, and a monthly plan covers the rest.
A short call, an honest answer on whether we are the right team, and a scope you can hold us to.
Start a project