# Neeve > Neeve is an enterprise AI engineering company. It identifies, designs, builds, and operates AI-powered solutions to real business problems. Neeve is not a consulting firm and not a systems integrator. The same bundle is served at neeveresearch.com and xplatform.com. Neeve is an affiliate of N5, not a division of it. --- ## Homepage URL: https://neeveresearch.com/ ### Hero Enterprise systems …built, powered, and operated with AI. Neeve is an enterprise AI engineering company. We identify, design, build, and operate AI-powered solutions to real business problems. ### Where we start The business problem comes before the technology. A great deal of enterprise AI work runs backwards. A technology is chosen, a use case is found to justify it, and the result demonstrates capability without changing how the business performs. We work in the other direction. We start with the problem, the constraints around it, and the outcome the business needs to reach, and only then decide what should be built and what it should be built with. Neeve is not a consulting firm and not a systems integrator. We are an engineering company. We find where AI creates value, design the solution, build it, and operate it in production. The same people carry the work from the first assessment through to the system running under load. The distinction matters in practice. Advice that stops at a recommendation produces a report. Delivery that stops at go-live produces a handover. *We stay with the system.* ### Enterprise AI engineering AI creates value in three distinct ways. **Definition.** Enterprise AI engineering is the discipline of applying AI across three dimensions of an enterprise system. - **How it is built** — the engineering of the system - **How it behaves** — the intelligence inside the system - **How it is operated** — the running of the system in production **The three dimensions are independent.** A system can be built with AI and operated by people. It can be built conventionally and powered by agents. Most enterprises need one or two of the three rather than all of them, and working out which is a business and solution assessment rather than a technology choice. The objective is never to put AI everywhere. **01 — AI builds systems. AI changes how enterprise systems are engineered.** It applies across design, implementation, testing, and the long tail of evolution. With AI, engineering accelerates, architectural patterns hold their shape across teams and across years, and the cost of building a system and then changing it falls together. Business value: accelerated engineering, consistent architectures, faster time to market, significantly lower development cost. Why Neeve: we bring Sutra, the AI system design and development agent, into customer engagements where it creates meaningful advantage. Where it is not the right fit, we build with whatever the customer's architecture calls for. **02 — AI powers systems. AI changes how enterprise systems behave.** Intelligent agents are embedded in the solution itself. They automate work that used to need people, support decisions that used to be made on incomplete information, improve how customers are served, and create capabilities the business did not previously have. Business value: intelligent automation, better decisions, better customer experiences, new business capabilities. Why Neeve: we apply AI in this dimension where it creates measurable business value, not because AI is available. Separating the two is a business assessment and a question of solution design, and it is the part that is easiest to get wrong. The business problem decides, and we will say plainly when a conventional approach is the better answer. **03 — AI operates systems. AI changes how production systems are operated.** Monitoring, diagnostics, optimization, deployment, and incident response move from AI-assisted to increasingly autonomous, under engineering supervision, at a consistency people cannot sustain around the clock. Business value: lower operational cost, reduced MTTR, higher reliability, continuous optimization. Why Neeve: we bring MCP servers and AI Ops agents into customer environments. We do not just recommend these technologies. We use them ourselves to operate production systems. ### Technology Technology follows the problem. We start with the business problem. The architecture is settled only once that problem is understood, and it is settled on the merits. We do not lead with a platform, and we have no quota to fill. Our ability to deliver differentiated solutions stems from our engineering approach, built on technologies developed by our affiliate N5. We bring Sutra, Rumi, operational AI agents, and MCP servers where they provide meaningful advantage. We know them deeply, and on the class of problem they were built for that advantage is large and measurable. The systems below are the evidence. Where they do not, we select the technologies best suited to the customer's objectives. *Independence is what makes the recommendation worth anything.* ### What Neeve Does From the first question to the running system. **Discover.** We work with business and technology leadership to understand the problem, identify the right solution, and determine where AI creates meaningful advantage. The output is a short list of candidate systems with an honest view of feasibility, cost, and expected return. **Build.** Design and build are one loop rather than two phases. AI-assisted development collapses the distance between choosing an architecture and running it, so decisions are tested in working code instead of being settled on paper first. The discipline around it stays conventional, with reviews, tests, benchmarks, and staged rollout. **Operate.** We run systems in production, through volatility, through failover, through growth. Telemetry from day one, increasingly automated diagnostics and response, and a team that knows the system because it built the system. ### Industries Where Neeve works. - **Capital Markets.** Equity trading, order management, exposure monitoring, risk. Electronic and high-touch flows at global banks and hedge funds. - **Banking.** Funds authorization and payment processing at banking core ISVs. Cloud-scale throughput with millisecond response budgets. - **Payments & Credit.** Transaction processing and authorization at card networks. High-volume, low-latency, zero-loss processing paths. - **Hospitality & Gaming.** Personalized pricing, dynamic offers, loyalty-driven commerce. Real-time pricing directly inside the transactional plane. - **Ad Tech & Real-Time Bidding.** Ad exchanges, DSPs, and SSPs operating at exchange scale, with sub-millisecond bid resolution and fault tolerance built in. - **Manufacturing.** Production scheduling, quality inspection, predictive maintenance, and supply chain visibility. Decisions taken against plant and sensor data as it arrives. ### Outcomes What systems built with Neeve deliver. Anonymized results from real systems across the industries above. **Electronic equities trading at a global investment bank** - $50M year-on-year cost reduction - 10× throughput increase (60× during Covid volatility) - 200+ → 6 servers in the order-management footprint - <3 yrs from first flow to full cutover **Consolidated exposure monitoring at a hedge fund** - <50 µs wire-to-wire tally latency (20× improvement) - ~6M orders per second, sustained - 2 servers total, one primary, one backup - 3 months from start to production **Funds authorization at a banking core ISV** - 55× transactions per CPU core (1,364 vs. 25) - 60× latency improvement (5 ms vs. 300 ms) - 300 → 4 servers, running on AWS - $1M → $9k annual infrastructure cost, at 60,000 req/sec **Personalized pricing at a hospitality and gaming operator** - 25× loyalty-driven revenue within 9 months - 20,000 personalized rates per second, in production - 1 ms pricing budget, including customer and channel data **Real-time bidding at an ad exchange** - 1.4 ms end-to-end bid resolution, across 8 message hops - Zero data loss through primary-to-backup failover - 3 commodity servers for the full exchange + DSP + SSP mesh ### In our customers' words > $50M year-on-year in cost reduction. Hardware, network, software, support, and trading risk. > — Global investment bank > We focus on the business. Neeve delivers the results. > — Enterprise data platform > High availability with performance we could not get anywhere else. > — Capital markets trading team ### How Neeve Engages A measured path from problem to production. **01 — Assessment.** We start with the business problem and the system that has to solve it. We look at what exists today, what it costs, what it cannot do, and where AI would change the answer. Where it settles the question faster than a document would, we stand up a preview system in hours. You get a clear recommendation backed by working software and measurable results. **02 — Pilot.** We build a representative slice in days rather than quarters and measure it against the system you have under realistic load. The pilot becomes the foundation of the production system. The results become the business case. **03 — Build or migrate.** For new systems we design and build end to end. For existing systems we move incrementally, with old and new running side by side through cutover. Neither path requires a freeze on current development. **04 — Production.** We operate the system with your teams. Capacity, failover, and recovery are proven before go-live, monitoring and response are increasingly AI-driven, and support continues for as long as the system runs. ### X Platform customers Nothing you built has been left behind. If you run X Platform today, the relationship continues as it is. The same engineering teams, the same deep knowledge of your trading engines, authorization services, bidding systems, and pricing platforms. Support continues, and there is no forced migration. Rumi is what X Platform became, and the path onto it is incremental, flow by flow, on your timeline. *Migration is one option, not a prerequisite.* Explore Rumi at https://www.rumi.systems ### Contact Talk to us. Whether you are working out where AI fits, replacing a system that has reached its limits, or running X Platform today, we would like to hear from you. - Email: contact@neeveresearch.com ### Explore the platform Our engineering approach is built on technologies developed by our affiliate N5. **Rumi™.** The No-DB Application Platform for building high-performance distributed enterprise systems. - Learn about Rumi at https://www.rumi.systems - Talk to the Rumi AI Expert at https://assistant.rumi.systems **Sutra.** The AI system design and development agent for engineering enterprise systems. - Launch Sutra at https://sutra.n5corp.ai **X Platform.** The platform Rumi evolved from, still running in production at existing customers. - Talk to the X Platform AI Expert at https://assistant.xplatform.com This is the single place on the site where visitors are invited to explore the underlying technologies, and it comes last, after the Neeve story. --- ## Systems Hub URL: https://neeveresearch.com/systems/ ### Real systems built on this foundation. Five anonymized records from real systems across capital markets, banking, advertising, and hospitality. Different industries, different demands, the same underlying architectural shift. 1. Equity Trading Transformation — electronic equities trading at a global investment bank 2. Funds Authorization — funds authorization at a banking core ISV 3. Exposure Monitor — consolidated exposure monitoring at a global hedge fund 4. Real-Time Ad Bidding — real-time bidding at an ad exchange 5. Value-Based Pricing — personalized pricing at a Fortune 500 hospitality operator --- ## Equity Trading Transformation URL: https://neeveresearch.com/systems/equity-trading.html PDF: https://neeveresearch.com/assets/case-studies/n5-rumi-ib-digital-transformation.pdf System: Electronic equities trading at a global investment bank. Lede: Replacing a fragmented, homegrown trading infrastructure with a single platform across the entire equity flow, on the bank's own timeline, without freezing the development of the existing business. ### Problem A homegrown trading stack at the end of its useful life. The system in production was a homegrown trading platform assembled over a decade and then strained by inheritance, with additional flows acquired through mergers, each built against a different set of internal infrastructural assumptions. By the time the bank started the rebuild, the consequences had compounded: performance below requirement, data loss on outages, feature velocity slowed by infrastructure maintenance, and a server footprint of more than 200 machines just for the order-management system. Roughly 60% of the development team's time was being spent on infrastructure rather than business logic. With trading alpha increasingly driven by data rather than infrastructure, that distribution had become untenable. The bank launched an initiative to revamp its equity trading system across every flow. ### Architectural Constraint The network between compute and state. Traditional multi-tier architectures separate compute, data, and messaging into distinct tiers connected over a network. For trading systems, that separation is the bottleneck. The volume and velocity of data is too high, and the latency budget too tight, to support fetching data across the network on every request. Industry practice had evolved to a different pattern, co-located compute and state communicating via message-passing between nodes. But no foundational substrate existed for it. Each trading team built its own from scratch. The bank's team had been doing exactly that for years, and the infrastructure they had built could no longer keep pace. ### Rumi solution A common foundation across front office and middle office. The bank standardized its next-generation equity trading system on Rumi. Rumi provides the substrate that the co-located-compute-and-state architecture was missing: data sourcing, persistence, encoding and decoding, HA consensus, message passing, exactly-once semantics, deployment, telemetry, and elastic scaling are all handled by the platform. The bank's developers wrote plumbing-free Java business logic, unit-tested in isolation, and promoted it to a distributed runtime through configuration alone. A single platform served front-office trade execution and middle-office services side by side, despite their different latency and high-availability characteristics. The rebuild proceeded flow by flow, with the existing system running in parallel during cutover. ### Operational Outcomes Performance, footprint, and cost together. - <9 months — first flow live in production - <3 years — full sunset of the legacy system - >10× — throughput on high-touch flows (60× sustained during Covid) - >10× — wire-to-wire latency reduction on zero-touch flows - 200+ → 6 — OMS server footprint, single consolidated OMS - $50M / yr — cost reduction across hardware, operations, and SLA penalties - Zero — data or message loss across multi-year operation - JVM & app — telemetry to per-message granularity with negligible overhead --- ## Funds Authorization URL: https://neeveresearch.com/systems/funds-authorization.html PDF: https://neeveresearch.com/assets/case-studies/n5-rumi-funds-authorization.pdf System: Cloud funds authorization at a banking core ISV. Lede: A two-tier authorization service rebuilt to meet cloud throughput and latency targets at orders-of-magnitude lower infrastructure cost, without changing the client-facing API. ### Problem The cloud version of the service was prohibitively expensive to scale. A banking core ISV was building the cloud-native version of its funds authorization service. The existing on-prem implementation followed the standard two-tier pattern: a compute layer hosting the authorization business logic, a database layer storing the customer and balance data needed by that logic, the two separated by a network. In a cloud deployment, the cost of scaling this architecture to the required throughput and latency was prohibitive, the kind of cost that determines whether the cloud version of the product is commercially viable at all. The ISV engaged Neeve to benchmark an alternative built on Rumi. ### Architectural Constraint You cannot fetch your way out of this. The bottleneck is the network between compute and data. Each authorization request requires several data fetches, each synchronous, each variable in size, each on a millisecond-or-better budget. The result is a low Transactions-Per-CPU-Core ceiling, and the only way to scale around it is to add cores to both tiers, horizontally. Faster databases, faster servers, faster networks each move the cost in the right direction, but none of them change the slope of the curve. To meet the cloud cost targets, the data fetch time needed to drop by multiple orders of magnitude. ### Rumi solution Compute and state in the same memory space. The new implementation runs the Account Service as a hyperconverged Rumi node. The compute that processes the authorization request and the customer data it needs sit in the same memory space. The data fetch is effectively free. Existing authorization business logic was ported into Rumi and unit-tested independently of the platform. The API exposed to clients was unchanged, so client systems migrated without code changes. Reliability, including primary/backup consensus and zero-loss replay across failures, comes from the platform rather than the application. ### Operational Outcomes Two orders of magnitude on cost; one on latency. - 3 weeks — initial port, sufficient for performance testing - 25 → 1,364 — Transactions per CPU Core (55×) - >300 ms → 5 ms — end-to-end latency (60×) - $1M → $9k — annual AWS cost at 60,000 req/sec (110×) - 300 → 4 — servers (75×) - Zero — loss recovery across network, process, machine, DC failures Note: Of the 5 ms end-to-end latency, 98–99% is attributable to Kafka in the request path. The Rumi-resident processing itself is sub-millisecond. --- ## Exposure Monitor URL: https://neeveresearch.com/systems/exposure-monitor.html PDF: https://neeveresearch.com/assets/case-studies/n5-rumi-hf-consolidated-risk.pdf System: Consolidated risk monitoring at a global hedge fund. Lede: A consolidated tally service that absorbs every order and trade from the fund's portfolio managers and computes risk metrics in real time, wire-to-wire in tens of microseconds, on two servers. ### Problem Independent PM decisions, fund-level imbalance. Portfolio managers at a global hedge fund manage their portfolios independently, with independent risk profiles, independent decisions on order slicing, long/short balance, and margin utilization. Run concurrently, that independence creates fund-level imbalances no single PM can see. The fund needed a consolidated exposure monitor to absorb every order and trade from every PM in real time, maintain running risk tallies, and feed those tallies back to the PM desks and market-connectivity engines that drive trade slicing. The fund started building it internally and concluded the infrastructure layer was taking more engineering than the decisioning logic itself. ### Architectural Constraint Tallies must update faster than the desk can use them. For the tallies to be useful, they must update in double-digit microseconds. Any longer and the upstream slicing decisions are made on stale risk. A traditional two-tier architecture cannot meet that budget. Each compute step must fetch the data it needs across the network from the data tier, on a synchronous critical path. Scaling around that requires multi-threaded concurrency and horizontal scaling of both tiers. And application-level consensus on failure is non-trivial to engineer; building it from scratch was exactly the kind of work the fund did not want to be doing. ### Rumi solution Storage, serving, streaming, and compute in one node. The exposure monitor was rebuilt as a hyperconverged Rumi node. The tally computation logic, the durable tally storage, the serving of tallies to the PM desks, and the streaming of tallies to market-connectivity engines all live in the same node, on the same data. The fund's tally calculation logic was ported in unchanged. Reliability, including primary/backup consensus and zero-loss replay across failures, comes from the platform rather than the application. Engineering time returned to the decisioning logic, which was the work the fund actually wanted to do. ### Operational Outcomes Tens of microseconds, six million orders a second, two servers. - 3 months — to first deployment - >1 ms → <50 µs — wire-to-wire tally compute latency (20×) - ~6M — orders/sec, sustained - 1 + 1 — primary and backup servers, <4 threads each - Linear — horizontal scaling with cluster partitions - Zero — loss recovery across network, process, machine, DC failures --- ## Real-Time Ad Bidding URL: https://neeveresearch.com/systems/ad-bidding.html PDF: https://neeveresearch.com/assets/case-studies/n5-rumi-ad-bidding.pdf System: End-to-end bid resolution at an ad exchange. Lede: Eight message hops, four micro-dataservices, sub-2-millisecond bid resolution end-to-end, and zero data loss through deliberate failover. ### Problem A financial market in everything but name. Real-time bidding for online ad impressions is a financial market in everything but name. Every page load triggers an auction; bidders must respond within tens of milliseconds; the system must process the bids reliably, sustain bursty traffic without degradation, and scale elastically across geography and campaign activity. Prior RTB implementations met those targets only with substantial custom infrastructure work, including bespoke I/O, threading, messaging, and serialization. These were built because the standard abstractions (servlet containers, ORMs, generic serializers) introduced too much latency and memory pressure. The cost was paid in engineering time, garbage-collection pauses, and large hardware footprints just to reach baseline. ### Architectural Constraint Bid decisions are data-intensive, and the data lives across a network. Bidding decisions are data-intensive, drawing on campaign rules, user profiles, and demand-side parameters. The data is large enough that fetching it across a network on the bid path is not workable inside a millisecond budget. Caching helps until durability becomes a requirement. Java systems pay an additional tax in GC pauses driven by the serialization and deserialization at the boundaries. As with the other systems on this page, the bottleneck is not the database or the compute. It is the network between them. ### Rumi solution Four micro-dataservices, one binary message bus. The full bidding system was built as four Rumi micro-dataservices, with SSP, Ad Exchange, DMP, and DSP each independently replicated for high availability and partitioned for horizontal scale. Inter-service communication runs over Rumi's binary message bus. The DMP holds hundreds of millions of user tracking records in memory. The DSP holds 1,000 active campaigns in memory. The Ad Exchange is co-located with the DSP, minimizing the most expensive step in the auction path. External clients see a standard HTTP/REST/JSON API. ### Operational Outcomes 1.4 ms across eight hops, on three commodity servers. - 3 servers — commodity hardware, dual 10-core 3 GHz, 96 GB RAM each - 1.4 ms — end-to-end bid resolution across 8 message hops - 24–62 µs — per-hop latency (DSP campaign evaluation at 463 µs dominates) - 1,000 / 100,000 — active campaigns and ad requests in the sustained test - <1 second — primary-to-backup failover; ~7% transient SLA breach - Zero — data loss through deliberate primary termination --- ## Value-Based Pricing URL: https://neeveresearch.com/systems/hospitality-pricing.html PDF: https://neeveresearch.com/assets/case-studies/n5-hospitality-gaming-value-based-pricing.pdf System: Personalized real-time pricing at a Fortune 500 hospitality operator. Lede: Per-customer pricing that uses reservation history, gaming spend, propensities, and offers to set rates at the moment the booking flow asks for one, at twenty thousand rates a second, in production. ### Problem An RMS optimizes the room. It does not see the customer. A traditional Revenue Management System maximizes room yield using room and market data, including historical demand, current inventory, and demand calendars. What it leaves out is the customer. A regular casino guest with a $300/night ceiling and $200/night in average gaming spend should see a different room rate during a high-demand fight night than a price-insensitive walk-in, not as a markdown, but because the system can mathematically value the customer's full trip and choose a rate that wins the booking while preserving overall margin. Doing this in real time, at the moment a customer requests a rate, would require pricing decisions that incorporate customer context inside the transactional plane, not in a nightly batch. ### Architectural Constraint Per-customer pricing does not fit in batch. Per-customer × per-property × per-room × per-day rates do not fit in batch. For a chain with 10 properties, 500 room types, a 365-day booking window, and an active base of 10 million customers, that is 18.25 trillion rates per pricing run, neither computable nor storable. The only feasible alternative is to compute the personalized rate at request time, against rack rates and customer data, inside the millisecond latency budget that modern booking UIs demand. That is a data-and-compute architecture, not just a faster pricing service. ### Rumi solution Pricing logic and customer data in the same node. Rack rates produced by the RMS (~1.8 million records) and the customer base (~10 million records) live co-located with the value-based-pricing logic inside a Rumi node. When a booking channel requests a rate, the system identifies the customer, the property, the room, and the dates, retrieves the relevant rack rate and customer record in memory, and runs the personalization logic to produce the final rate, all inside the request's response budget. Over time, channel-specific data was added on the same architecture, unlocking dynamic offer selection, OTA transactional pricing, product-bundle pricing, and consistent pricing across all channels. ### Operational Outcomes 25× loyalty revenue, 20,000 rates a second, one architecture. - 25× — increase in loyalty-driven revenue within the first 9 months - 20,000 — personalized rates per second, in production - 1 ms — pricing budget including customer and channel data - 1.8M / 10M — rack rates and customer records, co-located in node - All channels — OTA, Loyalty, Property, Contact Center, Front Desk - New CRS — same architecture extended to the in-house CRS --- ## Related sites - Rumi: https://rumi.systems - Rumi AI Assistant: https://assistant.rumi.systems - X Platform AI Assistant: https://assistant.xplatform.com - N5 Technologies: https://n5corp.com — an affiliated company, not a parent. N5 develops the platforms and owns the IP, including Sutra, Rumi, the operational AI agents, and the MCP servers. Neeve engages customers, delivers solutions, and licenses Rumi and Sutra as an authorized commercial channel.