We're going to walk you through RAG pipelines, eval systems, autonomous agents, and guardrails. This is enterprise AI that survives the real world. It doesn't just work on benchmarks. It scales without falling apart, and it actually makes your P&L better.
Here's what we've learned: standard RAG doesn't cut it at enterprise scale. We build systems that handle millions of documents, index in real-time, and work with images, PDFs, everything. Most importantly, they don't hallucinate when the stakes are highest: financial reports, legal contracts, compliance docs where getting it wrong is expensive.
We've seen this pattern too many times. Teams grab an off-the-shelf RAG system, assume retrieval is solved, and ship it. Then reality hits. They're chunking naively. One embedding model doing all the heavy lifting. No cross-document reasoning. No way to check if what you retrieved actually answers the question. Result: hallucinations spike, precision tanks when you need it most, and latency blows up real-time applications. And enterprise data? It's messy. It's interdependent. Multiple languages mixed together. Everything's legally binding. Missing even one clause in a contract opens the door to liability and costly downstream errors.
We don't just split on token boundaries. We look at the actual document, its structure, where the meaning boundaries are, what your industry cares about. Legal docs? We keep clauses together. Financial tables? We don't split columns. Medical records? We respect HIPAA requirements. The chunk is smarter.
BM25 nails exact term matches. Dense embeddings understand meaning. We use both, weighted for your domain. Some data needs precision (financial). Some needs understanding (support tickets). You get both, not one or the other.
We retrieve way more than we need, then aggressively filter. Cross-encoders score relevance. Business logic removes outdated stuff and deduplicates. What actually reaches your LLM is the good stuff. Fewer hallucinations. Faster responses.
PDFs with weird formatting? Tables that don't fit in plain text? Scanned documents where the text is barely readable? We've handled it. OCR for images. Table extraction that actually understands structure. Video transcripts aligned with timestamps. The full document, not just the easy parts.
What It Actually Delivers: We're handling 10M+ documents. P95 latency stays under 200ms. We've worked on repos with 50 years of history. English, Hindi, Spanish, whatever your customers speak. And here's the thing: you can update the index in real-time without blowing away everything and rebuilding from scratch.
Here's what we've learned the hard way: evals aren't a one-time thing. You need systems watching your models in production, catching drift before it becomes expensive. And "accuracy" isn't useful. Your CFO doesn't care about accuracy. They care about cost-per-error, revenue-per-automation, whether you're hitting SLA targets. We build evals that speak business language.
We see this too often. Team runs a test set once. Gets a 92% accuracy number. Ships it. Celebrates. Then the model drifts in production. User behavior shifts. Data distribution changes. And nobody notices for six weeks. By then you're hallucinating to customers, eating $500K in losses, and your reputation's damaged. But here's the deeper thing: high accuracy is a mirage. A model that's 99% accurate on small transactions but gets big ones wrong? That'll tank your P&L. You need to know what "good" means in your business. And almost nobody does.
What does a false positive actually cost you? False negative? Missing revenue vs. wrong compliance call? We sit down and map it out. Every prediction outcome gets a real business cost attached. Then we optimize for that, not for accuracy.
Not sampling. Not batch jobs that run weekly. Every inference gets scored in real-time against your cost function. Drift detection takes minutes. Alerts fire before customers notice something's wrong. You know about problems fast.
We compare current performance against baseline. Can spot when data distribution shifts. Find silent failures. And when you want to deploy a new model? A/B testing with statistical rigor makes it safe. You know the impact before you ship.
Not guessing. Not hoping. Every deployment decision backs up against eval data. "Should we roll this new model?" The dashboard tells you exactly what your P&L impact will be. That's how you decide.
What We Measure: Cost-per-error. Revenue-per-automation. SLA compliance. Drift rate. False positive costs. False negative costs. Customer impact. Data distribution shifts. None of that academic fluff. Everything ties to money and commitments.
Single agents hit walls fast. Real work is complex. We build multi-agent systems that choreograph across your business. Customer support escalations. Supply chain coordination. Financial compliance checks. Agents that know their boundaries, when to ask for help, and when to get a human involved. We're running 100K+ operations daily in production on this stuff.
A single LLM agent works for simple stuff. "What's my order status?" Fine. But the real world is messier. "Should we approve this loan?" That question requires checking credit scores, verifying collateral, reviewing payment history, searching for fraud signals, understanding the regulatory landscape. One agent gets confused. One agent can't escalate. One agent doesn't know it's in over its head. You end up making bad decisions. You need a team: different agents with different expertise, knowing when to hand off, knowing when to ask humans.
Fast intent detection. "Billing question or refund?" "Fraud?" Route it to the right specialist. Nothing fancy. Fast. Lightweight. High accuracy because it's doing one job.
Domain expertise. Support agent owns FAQs. Financial agent owns loan analysis. Legal agent owns contracts. Each one has access to the knowledge bases and tools that actually matter. Each knows what it can't do.
Agents don't just talk. They act. Query the database. Hit APIs. Create tickets. Update the CRM. Approve transactions (with guardrails). When I say autonomous, I mean it can move money, not that it's just generating text.
A meta-agent watches the specialists. Confidence drops? Go to a human. Agents disagree? Supervisor gets involved. High-stakes decisions? Humans decide. We're not trying to replace judgment calls.
What Happens in Production: We're running 100K+ operations daily. Each agent has guardrails built in: no making promises outside policy, no hallucinating. If an agent gets confused, it escalates. The system doesn't crash if one agent fails. Other agents handle it or a human jumps in. N8N means you can change routing logic without engineering. You own it, not us.
One bad hallucination to a regulated customer, and you're in compliance trouble. We build layers of protection: input filtering, PII redaction, output validation, cost limits, circuit breakers. It's the infrastructure that keeps your AI from breaking things.
Hallucination isn't a cute quirk. It's liability. In regulated spaces, it violates compliance. Exposes customer data. Kills trust. But here's the thing. It's not just the model. It's the whole pipeline. Inputs that aren't filtered can trick the model. Outputs that aren't validated make false claims. Unmetered usage spins into $50K monthly bills when the model misbehaves. Without guardrails, your cost-per-inference turns into a cost explosion when something goes wrong. And something will.
Before the input hits your LLM, we filter it. Prompt injection? Blocked. PII in the request? Masked. Content that shouldn't be processed? Classified and rejected. We rate limit. DOS protection. The model only sees safe input.
Everything the model generates gets checked before you see it. Does it match what's in your source documents? Is the JSON correct? Is the tone appropriate, not aggressive? Structured extraction makes sure it follows your schema. Bad output never leaves the system.
No customer names in responses. No addresses, SSNs, payment info. HIPAA compliance if you're in healthcare. PCI-DSS if you touch payment data. GDPR right-to-be-forgotten? Automated. Sensitive data is redacted before it goes anywhere.
Set budgets per user, per request, per hour. Costs spike? We throttle automatically. Tokens exceed threshold? Circuit breaker stops everything. You prevent runaway costs from a hallucinating model or a compromised endpoint. You control spend.
Regulatory: Actually Compliant RBI data localization. KYC requirements. SEBI for capital markets. DPDPA for India. HIPAA for healthcare. GDPR for EU. PCI-DSS for payments. We design guardrails that pass audits. Your regulators don't ask questions. Customers don't have complaints.
Start with an MVP. Scale to production. Everything comes with ongoing support and eval monitoring built in.
All tiers include: Full source code ownership · IP protection agreements · Non-exclusive licensing · Training for your team
We've done this enough times to know what works. Most vendors are still building prototypes when we're already in production.
We dig into your actual problem. Audit what you've already built. Find the data. Define success in revenue terms, not buzzwords.
1 week →We sketch the architecture. Lock down how evals will work. Pick models that actually fit. Map integration points with your systems.
1 week →RAG pipelines. Agent logic. Guardrails. Tests that matter. CI/CD that doesn't break things.
2-3 weeks →Test in staging. Move to production. Establish baselines. Evals start running for real.
1 week →Monitor in production. Tune the model. Cut costs. Teach your team how to run it themselves.
OngoingWe'll audit your current AI setup for free. Find gaps. Show you what production AI actually looks like in your business.
Free. No strings. NDA if you want one.