AI and software for service teams, across the queue, the phones, the answers your agents need in front of them, and the reasons customers keep contacting you.
The last row is the one that changes the numbers. A service desk that counts causes stops being a queue and becomes a signal.
In brief
EigenSpark builds AI systems and the software around them for customer service teams. We classify and route what arrives, assemble the order, payment and history into the ticket, and draft the reply from your current policy with the clause quoted. We read the calls as well as the tickets, so quality review covers the whole month. And we tag why each contact happened, so the causes get counted and sent to the team that can remove them. An agent sends every reply.
Your systems
Nothing here asks your agents to work in a second window.
Use cases
The queue first, then the answer, then the phones, then the reasons the contact happened.
Every ticket and message classified, routed to the right queue and matched against anything the same customer already has open.
Agentic AI →Order, payment, delivery and past contacts pulled into the ticket before an agent opens it, so the hunt across systems stops.
Systems Integration & APIs →A draft answer grounded in the policy document in force today, with the clause quoted, for the agent to check, edit and send.
Generative AI →The questions you get most, answered on your own site, app and WhatsApp from the same policy source your agents use, with a route to a person.
Agentic AI →Messages and calls handled in the languages your customers use, with the answer held consistent across all of them and reviewed by your own team.
Generative AI →Every call transcribed and scored against your own scorecard, so review covers the whole month and not a sample of six calls per agent.
AI & Data Strategy →Each agent given specific, quoted examples from their own recent contacts, with the behaviour to change named in one line.
Generative AI →Answers that agents actually used, turned into candidate articles for your team to approve, so the knowledge base follows the work.
Generative AI →Open contacts ranked by how close they are to breaking the promise you made, with the reason each one is stuck named.
Data Engineering & Platforms →Formal complaints logged, acknowledged and tracked against the timelines you owe, with the evidence for each step held with the case.
Continuous Compliance Monitor →Bills, warranty cards, proofs of delivery and claim forms read and checked against your policy, with only the doubtful cases passed on.
Document Intelligence Engine →Every contact tagged with what caused it, counted weekly, and sent to product, logistics or billing with the tickets attached as evidence.
AI & Data Strategy →Our own product
Our conversation intelligence platform reads recorded conversations, scores them against your own criteria and coaches the person who had them.
How an engagement runs
Get the context into the ticket, automate one part of the handling, then hand it over.
Two to three weeks connecting your service desk, telephony, order systems and policy documents, so the ticket opens with the customer, the order and the current rule already in it. Everything else on this page depends on this.
One part taken end to end: classification and routing, drafted replies, or call review. Small enough to prove in a quarter and measured against your own last quarter.
Categories, routing rules, escalation thresholds, policy answers and quality scorecards live in a console your service operations team runs. A new product or policy does not need us.
Training
Ninety-minute hands-on sessions, run on your own tickets, calls and policy documents.
Agents and team leaders
Drafting, summarising and checking against your own policy, practised on real tickets, including the ones where the policy does not answer the question.
Walk away withA drafting and checking routine for your commonest ticket types, usable on the next shift.
Service operations
Querying contact data directly, building the cause taxonomy, and reporting repeat contact in a way the owning team cannot argue with.
Walk away withA cause taxonomy applied to last month, and a repeat-contact report built on it.
The service head
Which questions may be answered without a person, how to read a deflection number, and how to introduce monitoring without the team gaming it.
Walk away withA written line between what a system may answer and what a person must.
Service teams usually take AI & Machine Learning and Data & Analytics, with Leadership & Executive Readiness for the service head. The full catalogue is in Training & Enablement.
The engineering half
Four layers of ordinary engineering. The first one decides whether a ticket can know who it is about.
A customer who emails, calls and messages is three records in most service desks. Joining them is what makes first contact resolution measurable.
Your service desk stays the system of record. Most of the effort is the telephony, the order systems and the channels customers choose for themselves.
Built for the person on the contact, with the draft, the clause and the customer history in one view, so nothing has to be looked up mid-call.
Defined once and used everywhere, so the service review and the product review are looking at the same causes. Your team runs and owns it.
We also run the training that goes with it: SQL and data engineering, cloud, enterprise systems and cybersecurity, alongside the AI tracks. A service operations team that can query its own contact data argues from evidence.
What we offer service teams
Most teams start with routing or drafted replies, and use three of the four.
Where a service programme starts.
Where a single workflow starts.
AI tracks, and the foundations under them.
Where the specifics change.
The specifics change by sector. See how this lands in BFSI or Retail & CPG.
Security and controls
An agent sends every reply, answers are grounded in your own policy, and recordings carry a retention date.
Drafts, summaries and suggested resolutions go to the person handling the contact. Where you choose to answer a question automatically, you choose which question.
Every drafted answer quotes the policy clause it used. Where the policy does not cover the question, it says so and routes to a person.
No refund, credit, waiver or compensation is issued by a system. It prepares the case, and your own approval chain decides.
Calls, transcripts and tickets hold personal data, so each carries its consent basis and its retention period, inside your own tenancy.
FAQs
No. A bot that answers on your website is one small part, and it is the part we advise you to switch on last. The work that changes the numbers is behind the desk: getting the customer and order context into the ticket, drafting from the policy in force, reading every call and no longer a sample of six, and counting why people contacted you at all.
Deflection on its own rewards a desk for not answering, and a customer who fails to get an answer comes back through a slower channel. We report it next to repeat contact rate and first contact resolution, because those three together tell you whether the customer was actually served. If deflection rises and repeat contact rises with it, the number is bad news.
No. Your service desk stays the system of record and your agents keep working in it. What we add is the context arriving in the ticket, the drafting, the call layer and the reporting. Writes go back through the desk’s own interfaces.
It tracks them and evidences each step. Under the e-commerce rules a complaint has to be acknowledged within 48 hours and redressed within a month, and regulated entities have their own ombudsman timelines on top. We hold those as configuration per business line, log the acknowledgement and the actions with dates, and flag a case before the clock runs out. The reply to the complainant is still written and sent by your team.
Yes, with your own team reviewing. We hold the policy answer fixed and adapt the language around it, so a customer reading Hindi and a customer reading English get the same entitlement. Where a language is thinly served by the models, we tell you before you promise it as a channel.
That depends entirely on what you do with it, and it is worth deciding before you build. What we build shows the agent the same score and the same quoted moment their supervisor sees, covers everybody equally, and is set up to remove work from the agent first. Where a system shows a supervisor something the agent cannot see, it gets gamed within a month.
No, and the parts that are not are usually what makes the AI work. Joining the customer records, connecting the telephony, getting the policy documents into one current set and building the routing rules are ordinary engineering, and they are most of the effort. The same is true of the training: alongside the AI tracks we run SQL, data engineering, cloud, enterprise systems and cybersecurity.
The context and drafting work pays for itself at around fifteen agents, because the saving is handling time multiplied by volume. The cause counting work is worth doing at any size, because it needs the tagging discipline more than it needs the volume.
Related
The services this is built from, the register it ships as, and where the specifics change.
Routing, self-service and the parts that act.
→ ServiceGetting order and payment context into the ticket.
→ ProductComplaint registers and the timelines you owe.
→ FunctionThe same conversation data, before the enquiry.
→ IndustryWhere grievance handling is regulated.
→ PillarThe engineering side, for the parts we build for you.
→Send them with your current policy documents. We call you within 48 hours and go through what your customers are actually contacting you about, how much of the handling time is the hunt for context, and what the first automation would be.
Talk to us