Automate work across sales, support, finance and operations. Splashgain designs, deploys and maintains AI agents connected to your systems, with your team in control.
Connect your existing records, business rules and approval process. Choose a workflow to see what the agent handles and where your team stays involved.
Have a different process? Bring your CRM, ERP, database or document workflow. We will review integration access, permissions and the actions that need approval before recommending an implementation.
The problem
Most AI initiatives die between the demo and the deployment
The demo is the easy half. What kills a project is the part nobody scopes — the system of record, the guardrails, and who gets paged when it stops.
What usually happens
A slick proof-of-concept that never touches a real system of record
Consultants deliver a strategy deck, then leave before anything ships
No owner for uptime — the agent silently stops and nobody notices for weeks
Data leaves the building; legal and compliance block go-live
Per-seat AI licences that scale cost faster than value
vs
What we do instead
Every engagement ends with a container running in production, not a PDF
We operate what we build — health-monitored, logged, on-call
Deployed on your cloud or ours; your data never trains a public model
Compliance-first architecture — retention, audit trail and access control from day one
A rules engine is cheaper, faster and never makes anything up. We reach for an agent only when a job passes one of these tests — otherwise we will tell you to write a cron job.
01
The decision needs judgement
Every case is slightly different, and the rulebook is really one experienced person’s instinct. A re-evaluation request. A refund on a disputed invoice.
02
The rulebook no longer fits on one page
Eligibility across forty programmes. Fee waivers with six exceptions each. The rules exist, but nobody dares edit them.
03
The input was written by a human, not a form
A handwritten answer script, a scanned mark-sheet, a WhatsApp message in Hinglish at 11pm. The work starts with reading, and that is where plain code gives up.
What comes in
“A ticket, a scanned mark-sheet, a WhatsApp message at 11pm”
One agent
Instructions
Your SOP, rewritten as steps it can follow, edge cases included
Model
Decides the next step. Never the final word on money
Tools
Your CRM, ERP and database — every one rated before it is wired in
Every tool is rated by what it can break
ReadsRuns alone
Reversible writesRuns alone, logged
Money, contracts, customer promisesDrafts — your person approves
Done, and written to the log
Handed to a person, with the trail attached
It hands over when it has tried twice and failed, or when the next action cannot be undone.
What we actually deploy
Agents that answer, decide and hand back — not chatbots that deflect
Each agent owns a task end to end: it reads the request, does the work against your systems, and escalates the cases it should not decide alone. We run every one of these in our own operations before we sell it.
Connected to your systems, not a sandbox
Escalation paths defined per agent
Deployed on your infrastructure or ours
Ready to scope an agent?
Pick one department, six weeks — we deploy one agent against one metric you care about.
We run our own AI agent fleet in production — every day
35+
AI agents running in our own production
2
Cloud regions, monitored 24×7
500+
Customers worldwide
17+
Countries served
AI employees
What you are actually hiring
An agent pack fills a role, not a tool slot. Here is the job, the shift, and the point where each one hands you the phone. Every role has a page of its own.
Watches the fleet, proves the jobs ran, and fixes the database first.
It acts alone only where the action is reversible or logged. Everything else waits.
What it does
Reports what each scheduled job produced, not merely that the server answered a ping
Hunts the slow queries and missing indexes before users start feeling them
Locks the database down by default and keeps an audit trail of every change
The eight packs below cut the same work the other way — by department, for when you already know which team is drowning.
How we build
Agents that act, inside guardrails you set
Everything we sell, we run ourselves first
Our sales team is prospected by our own agents. Our exam data is purged by our own retention agents. Our servers are watched by our own health monitor. You get software that has already survived contact with reality.
Your context, not the internet’s
Agents are grounded in your data, your policies and your tone. Generic models give generic answers — which is why most pilots stall before they reach production.
Auditable by design
Every action logged, every decision traceable. Built for regulators and boards, not just for demos.
Draft-and-approve by default
Anything touching money, contracts or a customer promise is prepared by the agent and approved by your person. Where an agent does act alone — deleting expired data, rebuilding an index — the action is reversible or logged.
The portfolio
Eight departments. Eight agent packs.
Packs are modular. Start with one, prove the number, expand. Nothing here requires a platform migration.
01
Sales
Revenue Pack
Your reps stop doing research, data entry and document assembly — and get back roughly a day a week to actually sell.
Prospect & enrich
Lead scoring
Cold outreach
Proposal & RFP
Quotation & SOW
Deal rescue
Meeting debrief
Tender war room
02
Marketing
Growth Pack
The output of a full content and SEO team at a fraction of the headcount — and visibility in AI search before your competitors realise it matters.
AI search visibility
SEO & ranking
Conversion (CRO)
Content engine
Competitor watch
Executive digest
03
Compliance
Retention Guardian
India’s DPDP Act makes “we kept it forever” a liability. This turns your retention policy from a document nobody follows into infrastructure that enforces itself — and cuts storage cost while doing it.
Most monitoring tells you a server is up. Ours tells you whether the work actually got done — and fixes the database before users feel it.
Fleet activity monitor
Database optimizer
Index maintenance
Database firewall
Uptime & incident
Read-only query agent
05
Knowledge
Company Brain
A private retrieval engine over your documents, tickets, contracts and wikis. When someone resigns, their knowledge stays — and every answer cites its source document.
Private document retrieval
Cited answers
Self-hosted vector store
Full query audit log
06
Finance
Finance Pack
Shortens the cash cycle and stops silent leakage. The vendor spend audit alone usually pays for the pack in month one.
Receivables & collections
Pricing & discount control
Vendor & SaaS spend audit
GST & filing calendar
07
Engineering
Engineering Pack
Raises code quality without adding reviewers. Every pull request is reviewed before a human spends time on it, and production errors are clustered rather than dumped.
We operate assessment, admissions and document-verification platforms ourselves. These agents are built on domain knowledge no horizontal consultancy has.
From first conversation to running agent in six weeks
The same five stages every time, whichever pack you start with. You approve the shortlist before anything gets built.
01
Discover
Two workshops to map where time and money actually leak. We name the metric we intend to move.
02
Prioritise
Score candidate agents on value against effort. You approve a shortlist of two or three — never twenty.
03
Build
Two-week sprints against your real data in a sandbox. You see working software, not status reports.
04
Deploy
Into production with monitoring, logging, rollback and a named owner. Team training included.
05
Operate
We run and tune it under SLA and report the metric monthly — or we hand it over. Your choice.
Commercials
Three ways to engage
Readiness Sprint
Fixed fee · 2 weeks · credited against deployment
Process and data discovery
Prioritised agent roadmap
ROI model per agent
One working prototype on your data
Executive readout
Pack Deployment
Build fee plus monthly operate fee · priced on application
Choose any pack from the portfolio
Configured to your systems and policies
Deployed on your cloud or ours
Monitoring, logging and SLA included
Unlimited internal users — no per-seat cost
Quarterly tuning and expansion
Agent Partner
Annual retainer · dedicated capacity · priced on application
A standing agent engineering team
New agents built on demand
Full fleet operated under SLA
Quarterly business review with the board
Source code and IP transferred to you
Priced against a metric, not a timesheet. Every engagement is priced against a named business metric. If the metric doesn’t move, we fix it at our cost.
Questions
What buyers ask us first
How is this different from an AI pilot?
Every engagement ends with a container running in your production environment, monitored and owned — not with a strategy deck. We operate what we build, so there is always someone accountable for uptime rather than an agent that silently stops and is noticed weeks later.
How do you know a scheduled job actually did something last night?
Most monitoring reports that a server is up. Our Fleet Activity Monitor reports per-container proof of work — messages sent, files migrated, records purged, errors thrown — which catches the job that runs successfully while doing nothing at all. A backup that backs up an empty folder still exits zero.
We are already doing AI internally. What would you add?
The model is rarely the hard part. Monitoring, retries, audit logs, access control and someone on call when it breaks at 2am are what separate a pilot from production. If those are already in place and running, you may not need us — if they are not, that gap is the work.
Our data cannot leave our premises. Is that a problem?
That is our default rather than an exception. Agents deploy as standard containers on your infrastructure, your data never trains a public model, and the audit log is yours. Company Brain in particular is designed to be self-hosted with your own vector store and access controls.
What happens when an agent gets it wrong?
Every agent is draft-and-approve by default on anything touching money, contracts or customer-facing communication — the agent prepares it and your person approves it. Where an agent does act alone, such as deleting expired data or rebuilding an index, the action is either reversible or logged, and we show you the log.
How long before something is actually running?
Six weeks from first conversation to one agent live in production. Most engagements start with a two-week Agent Readiness Sprint, which ends with a prioritised roadmap, an ROI model per agent and one working prototype built on your own data.
Do we get locked in?
No. Agents run as standard containers on your infrastructure. If you end the engagement they keep running, and on the Agent Partner engagement the source code and IP transfer to you.
One agent, or a team of them?
One, first. Most of our own fleet runs as a single agent with a handful of well-named tools. We split into a manager with specialists only when one agent starts picking the wrong tool — every extra agent is one more thing to monitor at 2am.
Tell us your department, the systems you use and the work that slows your team down. We will help identify a useful first workflow and arrange a relevant demo.