From the data you already have, to the outcomes you actually need. We run retrieval, support, and document intelligence systems for mid-market companies. Monthly pricing, measurable results, no internal AI team required.
You already have rooms full of data - documents, internal systems, customer interactions. Turning them into answers, search, and efficiency shouldn’t require hiring a team of AI or ML engineers.
Built, deployed, and running in production today.
All built
on a retrieval-first architecture (RAG, hybrid dense+sparse
search, grounded generation) that prioritizes accuracy and
citability over raw model capability.
Search that understands meaning, not just keywords. For e-commerce catalogs, product libraries, and internal knowledge bases where people search and don't find.
Deflect the easy tickets. Route the hard ones faster. We train an AI layer on your existing tickets and knowledge, then deploy it inside Zendesk, Intercom, or your custom stack.
Make your contracts, policies, and manuals answer questions. Used by operations, legal, compliance, and support teams who need answers from documents, fast.
Same retrieval foundation, different buyer and context.
Personalized merchandising and cross-sell for e-commerce. Same embedding infrastructure as semantic search, pointed at "what's next" instead of "what matches."
"Ask Evvara" for your own teams - answering questions about internal policies, procedures, past decisions, and operational context, with sources cited.
Put your CRM, past sales interactions, and deal history at your sales team's fingertips. "What have we sold this account?" "What objections came up with similar deals?"
Most mid-market teams we talk to can't tell, from the outside, whether their problem is actually an AI problem - or a data problem, or a product problem, or just a process problem. The honest answer is that sometimes it's one, sometimes it's another, and a thirty-minute conversation is usually enough to tell which.
Start a conversation - no obligation →We spend three to four weeks mapping your data, the workflows that hurt, and the outcomes that would move the number. You leave with a written plan naming what to pilot, what success looks like, and what it costs.
We build the workflow against a scoped slice of your business - one product category, one support queue, one document set. You see the system running on your real data inside a month. If it doesn't hit the metric, you own everything we built.
We run it. Monitor it. Tune it as your data and business change. You get a monthly report showing the workflow is doing what it promised, and an SME when something needs attention.
We've built and deployed AI systems that serve real users at real scale - not proof-of-concepts that never left a sandbox. When we say a workflow will work in your environment, it's because we've already run the same pattern in someone else's.
We're engineers who've shipped production software for fifteen years, including for enterprise clients like Bank of America. We pick AI tools based on what's stable and measurable today, not what's interesting at a conference.
Delivery and operations run out of New Jersey, Solapur, and Pune as a single team under shared technical leadership. Your primary contact is a senior leader - supported by an expert team.
We work best with mid-market companies - typically $5M to $300M in revenue - that have data-heavy operations and have hit the ceiling of what rules-based software can do. E-commerce teams with catalogs that search poorly. Support organizations with growing ticket volume. Operations teams buried in documents, contracts, and unstructured data.
Evvara is built on fifteen years of production experience serving enterprise clients - including Bank of America, PubMatic, Cognex, and Bitcentral.
Send a short note describing the problem, the context, and what would count as a good outcome. A senior member of our team will reply within one business day - with questions, a proposed next step, or an honest "we're not the right fit".
No calendar links. No sales sequences. No demos-on-demand. Just a conversation that goes somewhere or doesn't.
Each one converts something you already have - a catalog, a ticket stream, a document library - into something you can actually act on. Three core workflows we lead with, plus adjacent workflows for specific situations.
Search that understands meaning, not just keywords.
Traditional search matches the exact words a user types. Semantic search understands intent - so a shopper typing "gift for someone who just started running" finds running gear, not every product with the word "gift" in the title.
A US promotional products retailer had 4,100 SKUs across a catalog that had grown through acquisitions and internal category drift. Keyword search was returning noise: searching for "client appreciation" surfaced pens labeled "client," not the engraved gift sets that fit the intent. We deployed a hybrid system that blends semantic matching (embeddings) with keyword matching, tuned the blend for their category mix, and integrated it behind their existing search UI. Search-to-add-to-cart improved meaningfully within weeks.
Search-to-conversion (or search-to-click) is the primary metric. Secondary: zero-result rate, long-tail recovery. Typical pilots show 2–3× improvement in primary metrics, depending on how bad baseline search was.
Pilot is typically 4–6 weeks for a scoped catalog slice. Operate pricing scales with catalog size and query volume - typically mid-five to low-six figures annually, inclusive of embedding refresh, monitoring, and monthly tuning.
If your search problem is actually a catalog data problem (inconsistent tagging, missing descriptions, duplicate SKUs), no search system will fix it. Semantic search amplifies the quality of your underlying data - it doesn't substitute for it. In discovery, we'll tell you if data cleanup is the prerequisite.
Deflect the easy tickets. Route the hard ones faster.
Most support teams answer the same fifty questions over and over, while genuinely novel problems wait in the queue. AI customer support handles the repetitive layer, so your humans spend their time on the work that actually needs them.
A US-based OTT/broadcasting company was running a support operation on Zendesk, handling thousands of weekly tickets with high topical repetition - account access, billing questions, device compatibility, stream quality troubleshooting. Most questions had existing answers somewhere in the knowledge base or in prior ticket history; customers just couldn't find them. We built a layer that reads incoming tickets, retrieves relevant past resolutions and articles, and either auto-responds (for high-confidence matches) or prepares a suggested reply for an agent to approve.
Tier-1 deflection rate is the headline metric. Secondary: average handling time, first-response time, and CSAT parity - we monitor that AI-handled tickets don't erode customer satisfaction, which is a real risk if done carelessly.
Pilot is typically 5–6 weeks, scoped to one support queue or customer segment. Operate pricing typically ranges mid-five to low-six figures annually for mid-market support operations, inclusive of weekly tuning, safety monitoring, and monthly reporting.
If your support problem is primarily product problems - customers are contacting you because your product is broken, not because they need answers - no support AI will solve that. It will just make the repetitive complaints easier to close while the underlying issue compounds. In discovery, we'll map incoming tickets to root causes and tell you honestly what share of your volume AI can address versus what needs product work.
Make your contracts, policies, and manuals answer questions.
Most companies have hundreds of documents nobody has time to read - master service agreements, vendor contracts, compliance policies, product manuals, audit records. Finding a specific clause or policy takes hours. Document intelligence turns those documents into a system you can ask, in plain English, with sources cited.
This pattern - retrieval-augmented generation, or RAG - is the industry term for what we build here. Unlike pure LLM applications that can hallucinate freely, RAG systems retrieve real source documents first and generate answers only from that retrieved context, with citations back to the originals. It's the safer, more accountable pattern for any workflow where the cost of a wrong answer matters.
An operations leader needs to check whether a specific vendor MSA has a termination-for-convenience clause, what notice period applies, and whether any sub-clauses affect a specific deliverable. Today: thirty minutes of Ctrl-F through a PDF, a call to legal if it's ambiguous, a delay on the actual decision. With document intelligence: "What does our MSA with [vendor] say about terminating the services engagement for Project X?" - answered in ten seconds with the exact clause cited, document name, page number, and surrounding context linked.
Time-to-answer on document queries is the primary metric - typically dropping from 15–60 minutes to under a minute. Secondary: self-service rate (how often users get their answer without escalating to legal/ops/compliance), audit trail quality.
Pilot is typically 4–5 weeks, scoped to one document set. Operate pricing typically ranges mid-five to low-six figures annually depending on corpus size, update frequency, and number of user seats.
If your documents are themselves the problem - inconsistent, outdated, contradictory across versions - no retrieval system will produce reliable answers from unreliable source material. Document intelligence exposes the quality of your document governance. In discovery, we'll flag whether the corpus is ready or whether remediation comes first.
Personalized merchandising and cross-sell for e-commerce.
Same embedding infrastructure as semantic search, pointed at a different question: "given what this customer is looking at right now, and what they've bought before, what should we show them next?" We build next-best-item, cross-sell, and personalized merchandising into your existing e-commerce stack - Shopify, custom platforms, or headless commerce setups.
Typical engagement: 4–6 week pilot on one product line, then operated alongside your search workflow. Commonly taken on by clients who already have semantic search running with us, but we also take it standalone when the demand is there.
"Ask Evvara" for your own teams.
A conversational layer that answers your employees' questions about internal policies, procedures, past decisions, and operational context - with sources cited and access controls respected. Use cases range from HR policy lookups, to onboarding acceleration, to "what did we decide last year about X?" for long-running projects.
Related to document intelligence, but built for breadth of corpus (all your internal knowledge, not a specific document set) and conversational use (employees asking questions through Slack, Teams, or a dedicated interface). Typical engagement starts with one functional area - operations, HR, or engineering - and expands.
Your CRM and sales history, made askable.
Put your team's collective selling history at every rep's fingertips: prior account activity, previous objections, similar deal patterns, competitive context, product usage data. When a rep is preparing for a call, they can ask in plain English instead of searching six systems.
We integrate with your existing sales stack - Salesforce, HubSpot, Gong, Outreach - and respect your existing access controls. Typical engagement: 5–6 week pilot with one sales team or region, then operated across the sales organization.
Not sure which workflow is the closest fit to your problem? Start a conversation - discovery will tell you.
Start a conversationWe engage in three stages - a paid discovery, a fixed-fee pilot, and a monthly operate relationship. Each one has a clear end, and a clear way to walk away if the next stage isn't right.
We spend three to four weeks inside your business to understand three things: what data you actually have (not what you think you have - there's usually a gap), which workflows are hurting enough to be worth changing, and what outcomes would move the numbers your leadership cares about.
You leave with a written plan: which workflow to pilot first, what success looks like in measurable terms, what it will cost, and what the likely operate-phase cost structure is. If our honest read is that AI isn't the right tool for your situation - and sometimes it isn't - we'll tell you in the plan, and you'll still have the clarity you paid for.
Discovery is fixed fee and ends with a decision, not a commitment. Roughly a third of discoveries don't proceed to pilot - because the timing is off, the data isn't ready, or the problem is better solved without AI. That's a feature of the process, not a bug.
Once you've decided which workflow to tackle, we build it against a scoped slice of your business - one product category, one support queue, one document set. Enough to prove the outcome on your real data, small enough that we're never more than a few weeks from showing you something running.
The pilot has a defined success metric, agreed in discovery. If the system hits the metric, we move to operate. If it doesn't - and we'll know within the pilot timeline - you own everything we built (code, configurations, data pipelines) and we part as friends. No renewal pressure, no kicker fees, no proprietary traps.
The operate phase is where most AI services firms stop and most AI products never reach. We run the workflow in production on your behalf: we monitor it, we tune it as your data and business change, we catch drift before it becomes a problem, and we report monthly on the metric we committed to.
You don't staff an AI team. You don't manage infrastructure. You get a named point of contact - always a founder or senior engineer, never a rotating account manager - who knows your system and responds when something needs attention.
Operate is month-to-month after an initial six-month period. We commit to the six months because setup and tuning are front-loaded; we don't lock you in longer because if the workflow stops earning its fee, you should be able to leave.
We pick tools for stability and measurability, not novelty. Our current default stack:
If one of the above is what you need, we're happy to refer you to firms that do it well.
Ready to start with a discovery?
Start a conversationA fifteen-year-old digital marketing practice that runs paid campaigns, organic search, and generative engine optimization for mid-market brands. Operated the same way our AI work is: with clear metrics, honest reporting, and a focus on what actually moves the number.
Paid acquisition, organic visibility, and the new category that sits between them - optimizing how your brand surfaces inside AI answers. Each engagement is managed by a senior operator, not a junior pool.
Paid acquisition across Google, Meta, LinkedIn, and programmatic channels. Campaign strategy, creative direction, daily management, and weekly performance reporting.
Technical SEO, content strategy, link architecture, and ongoing optimization. Measured by organic traffic, keyword rankings, and conversion from organic sources - not vanity metrics.
Optimizing how your brand surfaces inside AI answers - ChatGPT, Claude, Perplexity, Gemini. A new discipline, increasingly critical as search behavior shifts toward conversational interfaces.
Setting up the measurement infrastructure behind everything else. What's working, what isn't, and where spend should move. Applies equally to new campaigns and to cleaning up attribution on existing ones.
Digital marketing engagements start with a two-week audit - we look at your current campaigns, organic presence, and analytics setup, and come back with a plan including the channels we'd focus on, the metrics we'd commit to, and what we'd change in the first sixty days.
From there, we engage on a monthly retainer scaled to your scope: typically mid-four to mid-five figures per month depending on channel mix, spend level, and organic ambition. Every engagement includes a named senior operator who runs your work day-to-day, not just an account-management veneer.
We work best with companies spending $3K–$100K/month across paid channels, or with meaningful organic visibility to protect and grow. Most of our clients are in e-commerce, SaaS, B2B services, and mid-market consumer brands - places where every dollar of spend has to return measurable value, and where generic "growth marketing" agencies have already been tried and found wanting.
Start with an audit. We'll tell you what's working, what isn't, and where to move first.
Start a conversation
Now pointed squarely at a question worth answering:
What does it take to make AI actually work inside a real business?
Evvara is co-founded by Harshul and Achal, who have worked together as business partners for fifteen years - first as co-founders of Eywa in 2011, now as co-founders of Evvara.
They work as a single leadership team on every engagement. Both report into a shared technical and operational spine.
Eywa was founded in 2011 as a software services firm serving enterprise clients. Over fifteen years, we built and ran systems for companies including Bank of America, US-based promotional products and OTT businesses, and a range of mid-market clients across North America.
In early 2026, we began the work of pointing the practice at a single, more focused question - how AI changes what a mid-market services firm should look like.
Evvara is the name for what comes next. The same team. The same fifteen years of delivered work. A narrower, sharper focus.
While we continue to grow Eywa for what it's best at - rules-based software.
We built Evvara around two deliberate choices, all of them out of step with how most AI services firms are being built right now:
If that resonates - if you're trying to make AI actually work inside a real business and you're tired of demos that don't become systems - we'd be glad to hear from you.
Tell us what you have in mind.
Start a conversationWe reply within one business day. Every inquiry is read by a human.
New Jersey, United States
Solapur & Pune, Maharashtra, India
Every inquiry is read by a senior team member (human). We'll either reply with questions, a proposed next step, or - if we're not the right fit - a direct note saying so. No calendar links, no email sequences, no surprises.