Production-grade RAG, copilots and autonomous agents on Claude, GPT, Gemini and open-source LLMs. With evals you can trust, guardrails you can audit and observability you can debug.
"AI agent" is fuzzy. These six concrete patterns cover 90% of what businesses actually need. We'll help you place yours.
Knowledge-grounded chat trained on your docs, tickets, PDFs and Notion. Answers in your tone, cites its sources, refuses when uncertain.
Multi-step agents that pull data, call APIs, draft outputs and stop for human approval at the right step. Replaces brittle Zapier chains.
Domain-specific code generators tuned to your codebase patterns, naming conventions and review standards. Beyond generic Copilot.
Invoices, contracts, statements, prescriptions, ID cards. OCR + LLM extraction with structured JSON output and human review queue.
Lead qualification, meeting prep, email drafting, ticket triage and CSAT analysis agents. Plugged into HubSpot, Salesforce, Zendesk and Intercom.
Phone, voice and vision-enabled agents using GPT-4o, Gemini, Whisper and ElevenLabs. For customer support, telemedicine and field operations.
Model-agnostic by design. Most agents we ship can swap between Claude, GPT and Gemini with a config change.
Most LLM proofs of concept never ship. Here's the gap, and how we close it on every build.
Most teams ship by gut feel and roll back when it breaks. We build a golden dataset with your subject-matter experts in week 1, then run automated regression on every prompt, model and retriever change.
We instrument every LLM call with full prompt, response, tool use, latency, token count and cost. Production issues become searchable, not anecdotal.
Production LLMs face hostile users. We engineer for adversarial inputs, sensitive data leaks and silent hallucination from the start.
Workshops with subject-matter experts. We pin down the exact decision the agent will make, the success metric and the golden dataset structure.
A working agent on 1 to 2 models, scored against a 50 to 100 case golden set. You see real metrics in week 3, not vibes.
Prompt + retriever + model tuning, tool use, guardrails, structured outputs, retries, fallbacks. Every change measured against the eval set.
Plug into your product surface (web app, helpdesk, CRM, Slack, voice). Build the human-review queues and feedback capture UI.
Canary on 5% traffic, monitor evals on live data, then graduate to 100%. Cost dashboards, alerts and runbook delivered.
RAG copilot deflects 38 to 62% of tier-1 tickets
95%+ accuracy with human-review queue for rest
Inbound scoring + auto-draft of first outreach
Natural-language Q&A on your BI warehouse
Pre-screens contracts, claims, KYC docs
IVR replacement, appointment booking, follow-ups
Meeting prep, deal coach, follow-up drafts
Article research, briefs, fact-checks, citations
Depends on the task. Claude tends to lead on long-context analysis, careful reasoning and instruction following. GPT-4o is excellent on multi-modal and reasoning. Gemini Flash is best on cost-per-token at scale. We benchmark all three on your golden set in week 2 and let the data decide. Our architecture lets you swap with a config change.
Three layers. (1) Grounding: RAG with citations and "I don't know" as an allowed answer. (2) Validation: JSON schema and rule-based checks on every structured output. (3) Eval: a golden set that explicitly tests for hallucination and a CI gate that blocks regressions. We can also add a second-pass critic LLM for high-stakes outputs.
Yes. For regulated industries (healthcare, fintech, government), we deploy open-source models (Llama 3, Mistral, Qwen) via vLLM or Ollama on your hardware, with all data staying inside your network. Performance is usually within 10 to 20% of frontier models on focused tasks.
Discovery + prototype: $9,500 to $18,000 (2 to 4 weeks). Production-grade single-agent build: $35,000 to $90,000 (8 to 14 weeks). Multi-agent platform with eval, observability and ops UI: $90,000 to $250,000. Most agents pay back inference cost from operational savings within 4 to 7 months.
You do, on your own provider accounts (OpenAI, Anthropic, Google, AWS Bedrock). We never proxy your tokens through us, so you keep full data control, billing visibility and the ability to switch providers. We help you set the right rate limits and cost budgets.
Every production agent we build has: (a) prompt-injection detection on user inputs, (b) PII redaction before LLM calls, (c) allowed-tool whitelist with output sanitisation, (d) JSON-schema validation on structured outputs and (e) human-in-the-loop gates on irreversible actions. We do not let agents directly delete data, send irreversible emails or move money without explicit confirmation.
Yes, deliberately. We wire a feedback loop (thumbs up/down, optional comment) into every agent and review the bottom 1 to 5% of interactions weekly. New examples get added to the golden set, prompts and retrievers tuned, and the improvement is measurable. We see 15 to 35% quality lift between v1 and v3 typically.
Book a 45-minute use-case clinic. Bring a real workflow you want to automate. We'll tell you honestly whether an agent is the right fix.
If an agent isn't the right answer, we'll say so. We don't sell hammers in search of nails.