Look, we handle RAG pipelines, evaluation systems, autonomous agents, guardrails, the whole stack. But here's the difference: we don't ship demos. We build systems that actually make money at scale. And we're the only semi-SaaS AI agency where you'll get a team that owns the outcome if something breaks at 2am.
We don't start with models. We start with money. What does a mistake cost you? How much do you save per automation? Those are the numbers we optimize for, not vanity metrics that mean nothing to your CFO.
We're building retrieval systems for enterprises that can't afford hallucinations. Millions of documents. Multi-modal data. Real-time indexing. Your financial reports, legal contracts, and compliance records aren't toys. We treat them that way, with hybrid search strategies and re-ranking that actually works.
Here's the part everyone skips: continuous evaluation tied to what actually hurts your bottom line. Not one-time tests that give you false confidence. We're talking living systems that flag drift before it costs you real money. And we define "good" in a language your finance team gets: cost per error, revenue per automation.
Multi-agent systems that actually know the difference between "handle this" and "escalate to a human." Customer support chains that don't waste engineer time on simple questions. Supply chain optimization that coordinates across six different systems without breaking. Agents with judgment.
One bad hallucination to a regulated customer? That's your license. We're talking content filtering, PII protection, output validation, and circuit breakers that actually work. Cost controls that stop runaway token spend. Because in production, "oops" isn't an acceptable error mode.
We're not married to any single model vendor. We pick the right tool for the right problem: Claude for reasoning, GPT-4o for multi-modal, Bulbul 3.0 for Hindi-English code-switching. Then we build the orchestration layer that ties it all together without vendor lock-in.
Sometimes we're starting from scratch. Sometimes we're inheriting a failed pilot and actually shipping it. Either way, we're doing it in weeks.
500K contracts. Clause extraction that doesn't miss amendments. Cross-jurisdictional compliance checks that actually work. Your lawyers shouldn't be reading every contract twice. We've built RAG systems that handle that.
KYC agents that don't leak PII. Fraud detection that actually flags anomalies in real-time. Regulatory reporting that makes RBI audits easier, not harder. We've built this enough times to know what works and what doesn't.
Patient intake that doesn't leak medical records. Clinical trial matching across millions of patient profiles. Insurance claims that actually get approved on the first submission. Audit trails that regulators want to see.
10M SKUs? We're enriching those. Dynamic pricing that responds to inventory and demand in real-time. Support for Hindi-English code-switching that actually doesn't suck. Bulbul 3.0 handles what generic models completely miss.
Demand forecasting that actually doesn't get surprised by demand. Vendor risk scoring that catches financial distress before your contracts do. Multi-agent systems that coordinate across procurement, warehousing, and delivery without human intervention every step.
100K tickets a month. We're solving tier-1 questions without bothering your humans. Tier-2 problems that need judgment get flagged for escalation in seconds, not hours. CSAT actually goes up because we know when we don't know.
400+ compliance officers. 15,000 applications daily. 12% error rate. Two RBI warnings in 18 months. This bank had already burned ₹2Cr on a big-4 consulting project that shipped a prototype unable to handle Hindi, Marathi, or Tamil documents, and of course, there's no eval framework. We rebuilt the whole thing: multi-agent KYC with Bulbul 3.0 handling Indic OCR, Claude doing reasoning over edge cases, and continuous evals that actually catch drift before regulators notice. PII protection? Circuit breakers? We've got that. Human reviewers get escalations in 90 seconds for anything the system isn't sure about.
"Fourteen months of consulting and we still had nothing live. These guys? Three weeks and we're in production with evals running. The continuous eval dashboard was worth paying for all by itself."VP of Digital Transformation, [Bank Name Redacted]
2,000 contracts a week. Their in-house NER model hit 78% on English and completely fell apart on cross-border documents with mixed language. We're talking 800K contracts in their corpus, growing linearly with headcount. So we built hybrid RAG: LlamaIndex with legal-specific tokenizers, Pinecone handling semantic search at scale, Claude doing the reasoning over clause interactions. But here's what mattered: the eval framework tracked what actually costs them: missed risk clauses (we call that "liability"), false positives (wasted lawyer hours), and SLA breaches. Not just accuracy percentages.
"The evaluation approach completely changed how we think. We're not chasing accuracy percentages anymore. We're asking what a miss actually costs. That mental shift was worth more than the technology."CTO, [LPO Firm Name Redacted]
8M monthly users. ₹85 per ticket. CSAT stuck at 3.1/5. 48-hour wait times. They'd tried three chatbot vendors and all of them failed because 62% of tickets involved Hindi-English code-switching, which these generic tools simply don't handle. So we built tiered agents: Bulbul 3.0 for intent classification, N8N handling the routing logic, Claude for the hard cases that need judgment. Every response gets validated by Guardrails for tone and factual accuracy. Real-time evals track CSAT, time-to-resolve, escalation rates, and cost per ticket.
"Bulbul 3.0 actually handles Hindi-English code-switching. Nobody else comes close. And our ops team can adjust routing through N8N without waiting for engineers. That's huge."Head of CX, [D2C Brand Redacted]
$4.2M annual losses from supply chain chaos. 14 plants. 200+ suppliers. Their SAP system couldn't see what was actually happening: port delays, commodity price swings, or that one supplier was about to fold. We built multi-agent supply chain intelligence: ingestion agents pulling from SAP, shipping APIs, commodity exchanges, news feeds; analysis agents running demand forecasting with ensemble models; action agents generating purchase orders and risk alerts. Everything ran on-premise with OpenClaw (data sovereignty requirements), and N8N orchestrated the workflows with human approval gates for critical decisions.
"Our agents caught supplier bankruptcy three weeks early. We'd already diversified our orders before the news hit. That one alert paid for everything."COO, [Manufacturer Name Redacted]
We're actually based here. We understand the real constraints: code-switching users, data localization requirements, cost-per-token sensitivity, and regulators with teeth. That's not theoretical for us.
India's most capable multilingual model. It's not just "speaks Hindi." It's Hindi, Tamil, Telugu, Marathi, Bengali, Kannada, and 16 other languages with real domain expertise. We've fine-tuned it for legal, financial, and healthcare terminology that generic models completely miss.
OpenClaw for on-premise. Your data doesn't go anywhere. We design knowing RBI's rules, DPDPA compliance, and whatever sector-specific regulations apply. This isn't a feature we added. It's how we architect from day one.
India's budgets work differently. So we build cost-per-inference as a first-class metric. Bulbul 3.0 for high-volume stuff. Claude for reasoning-heavy problems. Smart routing that cuts your LLM spend by 65-80%. Because every rupee counts.
Your ops team shouldn't be waiting for engineering tickets to adjust routing logic. N8N lets them modify workflows themselves: escalation rules, approval gates, agent routing. Drag-and-drop. Because the best system is the one your team can actually manage.
Relevance AI and generic platforms work fine for simple stuff. But if your problem is complex enough to hire an agency, it's too hard for their templates.
| Capability | Automation Agents | Relevance AI | Generic Platforms |
|---|---|---|---|
| Custom RAG pipelines | ✓ Built from scratch | Template-based | Not available |
| Continuous evaluation pipelines | ✓ Business-cost-aware | Basic metrics | None |
| Multi-agent orchestration | ✓ N8N + custom | Limited chains | Single agent |
| India multilingual (Indic) | ✓ Bulbul 3.0 native | English-first | Translation layer |
| On-premise deployment | ✓ OpenClaw | Cloud-only | Cloud-only |
| Guardrails & compliance | ✓ RBI/SEBI/DPDPA | Basic content filter | Minimal |
| Enterprise scale (1M+ ops/day) | ✓ Proven | Unproven at scale | Rate limited |
| Model agnostic | ✓ Any model | Limited models | Single provider |
Not "here's a platform, have fun." We design it, build it, ship it, evaluate it, maintain it. If it breaks at 2am on a Sunday, we're waking up, not your ops team.
Every single system ships with continuous evals tied to your actual business metrics. You'll see performance drift before your customers complain about it.
SOC 2 audits. Data localization requirements. Change management. We've had those conversations. We know the process.
Bulbul 3.0. On-premise deployment. Cost-optimized architecture. But built to Bay Area reliability standards.
87% of AI pilots never make it to production. The missing link isn't better models. It's the absence of an evaluation approach that maps accuracy to revenue. Here's the methodology we use.
Read → 8 min False SignalsThat 95% accuracy on your test set? It's a snapshot, not a system. One-time evals mask model drift, data distribution shifts, and the slow bleed of production quality.
Read → 6 min Accuracy TrapA model with 99% accuracy that's wrong on your highest-value transactions is worse than one with 90% accuracy that never misses the big ones. Cost-weighted evals change the game.
Read → 7 min Defining GoodYour CEO doesn't care about F1 scores. They care about cost-per-error, revenue-per-automation, and time-to-value. How to build eval metrics that executives actually read.
Read → 10 min Hidden TaxEvery AI system without continuous evals is accruing technical debt that compounds. We calculate the real cost of "deploy and forget." It's worse than you think.
Read → 9 min OwnershipIf nobody owns the evaluation process, nobody owns the AI quality. How to structure eval ownership across data science, engineering, and product teams.
Read → 7 min Build vs BuyBuilding eval infrastructure is a capital investment, not an engineering side project. The strategy for deciding when to build, when to buy, and when to hire an agency.
Read → 11 min PerfectionismPerfect is the enemy of deployed. Over-engineering eval systems delays production and burns budget. The 80/20 rule for eval frameworks that actually ship.
Read → 6 min RiskIn regulated industries, evals aren't optional. They're your insurance policy. How continuous evaluation prevents the catastrophic failures that end careers.
Read → 8 min ProductionWe've seen companies lose $500K+ because a model started hallucinating in production and nobody noticed for 6 weeks. The eval system that would have caught it in 6 minutes.
Read → 9 min SystemsComponent-level accuracy means nothing if the system fails. End-to-end evals that measure what the customer actually experiences and what it costs you when it breaks.
Read → 10 min Profit EngineThe companies winning with AI aren't the ones with the best models. They're the ones with the best eval frameworks. How to make the business case for eval investment.
Read → 12 minGet a free AI audit from us. We'll map what you've got, find the gaps, and show you what production-grade AI actually looks like for your specific business.
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