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Production-grade agentic AI built by a 20-year engineering team. Autonomous agents, multi-agent systems, RAG, tool-calling: stuff that actually runs in production, not just demos. We're not an AI startup learning on your dime.
Agentic AI is the next layer above generative AI. Systems that don't just answer prompts, but plan, act, and adapt across real-world workflows.
Agentic AI development means building AI systems that actually do work. They take a goal, break it into steps, call APIs and tools, pull context from your data, check their own output, and either finish the job or hand off to a human. Traditional AI answers a question. Agentic AI gets a job done.
| Capability | Agentic AI | Generative AI | RPA | Chatbot |
|---|---|---|---|---|
| Plans multi-step goals | ✓ | — | Scripted only | — |
| Takes real actions in your systems | ✓ | — | ✓ | Limited |
| Reasons about ambiguous input | ✓ | ✓ | — | Pattern match only |
| Adapts when the environment changes | ✓ | — | Breaks | — |
| Uses tools, APIs, databases autonomously | ✓ | — | Scripted | — |
| Best for | Complex knowledge work | Content generation | Stable repetitive tasks | FAQ-style queries |
Honest advice from a team that says no when no is the right answer. Most agentic AI projects fail because someone built a multi-agent swarm when a SQL query would have done the job.
From a 1-week feasibility scope to production multi-agent systems running in your VPC. Eight focused service lines, one accountable team.
Purpose-built agents for one specific job: claims processing, document review, sales SDR, AP automation. We ship with guardrails, evals, and observability from day one. The architecture survives past the demo.
A planner. A retriever. An executor. A verifier. We wire up the right agents for workflows that span multiple systems and decisions.
Agents that pull from your documents and proprietary data. Every answer is citable. No model hallucinations dressed up as fact.
Agents that take real actions. Read your CRM, write to your ERP, call third-party APIs. Every tool call goes through structured outputs and schema validation. Integration isn't an afterthought, it's where most projects break, so we treat it as a first-class engineering problem.
1-week fixed-bid discovery, ROI modeling, architecture review, vendor selection. You get a go/no-go in writing.
Domain tuning plus automated eval harnesses (RAGAS, DeepEval, LangFuse). We catch regressions in CI, not in production.
Audit logs, latency and cost telemetry, drift detection, rollback playbooks. The boring stuff that keeps agents alive.
Performance tuning, prompt updates, model swaps, quarterly optimization sprints. We don't disappear after launch.
We build agentic AI workflows that survive real-world constraints. Compliance audits, integration complexity, the demand for explainable decisions.
Real agentic AI workflows in production. Specific, scoped, measurable.
Reads claim documents, extracts entities, validates against policy, flags fraud signals, drafts adjuster notes.
Researches prospects, drafts personalized outreach, books meetings on the rep's calendar, hands off qualified leads.
OCRs invoices, matches PO + receipt (3-way), routes for approval, posts to ERP. Built for AP teams drowning in PDF.
Conducts conversational intake, validates insurance, books appointment, notifies scheduler. HIPAA-compliant by design.
Tier-1 inquiries handled end-to-end. Order lookup, returns, exchanges, escalation routing, with full conversation audit.
Reviews contracts against your playbook, flags risky clauses, suggests redlines, summarizes obligations for legal teams.
Reviews PRs against style + security rules, flags regressions, suggests fixes, posts findings to GitHub. Doesn't merge (yet).
Answers employee questions from your wiki, Slack, Notion, and SharePoint. Cites every source. Says "I don't know" honestly.
Monitors inventory thresholds, predicts stockouts, drafts POs, communicates with suppliers, escalates exceptions to humans.
Most agentic AI projects fail in production. Not in the demo. We engineer for the math, not the marketing.
An agent that's 85% accurate per step is not 85% reliable. Errors compound. Put 10 steps in a row and your end-to-end success rate drops to 0.8510 = 20%. That's the moment most "demo magic" agents fall apart in real work.
| Per-step accuracy | 5-step workflow | 10-step workflow |
|---|---|---|
| 80% | 33% | 11% |
| 85% | 44% | 20% |
| 95% | 77% | 60% |
| 99% | 95% | 90% |
So we engineer for per-step accuracy first, structured outputs second, and "feel-good demo" never. Gravitee's 2026 State of AI Agent Security found 3M+ agents in production, and only 47.1% are monitored. Which is insane.
Every tool call validates against a strict schema. Free-text agent outputs in production are a bug we don't ship.
Agents answer from your data, via vector search and hybrid retrieval. Not from the LLM's training memory. Every claim is citable.
RAGAS, DeepEval, and LangFuse run on every prompt and graph change. We catch regressions in CI, not in production.
High-impact actions (sending money, modifying records, customer-facing replies) gate on human approval until confidence thresholds prove themselves.
Every decision is replayable. We log prompts, retrievals, tool calls, outputs, and verdicts. For compliance, debugging, and trust.
Stop-gates after every phase. If the value isn't there by Phase 2, you walk away. Not after a year of spend.
Workflow audit, KPI definition, fit assessment. Fixed-price 1-week scope. You get a go/no-go in writing.
Agent design (single, multi-agent, RAG), tech stack picks, and a working pilot you can stress-test on real data.
Production-quality agent with guardrails, evals, prompt suite, and core integrations. Internal demo when the phase wraps.
Failure-mode testing, observability wiring, security review, shadow-mode against live traffic before we hand over autonomy.
Live deployment with audit logs, monitoring, and a continuous-improvement cadence. We don't disappear at launch.
We're LLM-agnostic and cloud-flexible. Architecture first, then the right tool for your constraints.
LangChain LangGraph CrewAI AutoGen 
LlamaIndex 
PydanticAI ReAct DSPy Semantic Kernel 
MCP 
Claude 3.5 Sonnet Claude 3 Opus 
Llama 3 
Mistral Large 
Gemini 1.5 Pro Cohere Command R+ DeepSeek 
Open-source fine-tunes 
Qdrant Chroma pgvector 
FAISS Redis Hybrid (BM25 + dense) Gemini Enterprise Hugging Face 
Anthropic API NVIDIA NIM LangSmith Helicone Phoenix (Arize) 
Datadog LLM Obs 
OpenTelemetry 
Docker 
Kubernetes 
Terraform 
FastAPI 
Helm 
GitHub Actions Azure DevOps 
Python Tell us about your workflow. Get a tailored projection of annual savings, payback period, and 3-year ROI for a custom agent built around your team.
Most vendors hide pricing behind "request a quote." We publish ranges because it's faster for both of us.
Indicative outcomes from a recent Trango Tech engagement with a top-25 US health-insurance carrier. Client name's under NDA. Full reference call available on request.
A regional health insurer's claims team was drowning. 11K weekly claims, 38% needing manual review, a 9-day average cycle, and a backlog that kept growing. Outsourcing wasn't an option because of HIPAA.
A 3-agent system. A Reader agent pulls structured data out of claim forms. A Validator agent checks against policy and medical-necessity rules using RAG. A Decision agent auto-approves the clean ones and routes the ambiguous ones (with summarized context) to human adjusters. Full audit log on every step.
Structured-output validation on every tool call. RAGAS-gated deploys. Shadow-mode rollout for the first 4 weeks. Human-in-the-loop on any claim above the dollar threshold. Zero compliance incidents wasn't luck. It was the design.
Research, personalize, book. Fully autonomous outreach, with human approval gates on calendar invites.
OCR + 3-way matching + ERP posting. AP team redirected to vendor management instead of data entry.
Order lookup. Returns. Refunds. Fully resolved with audit-logged conversations. Escalation routing for the rest.
Nine reasons that show up in production. Not in the pitch deck.
Most agentic AI vendors are 2–3 years old. We've been shipping software since 2004. There's a difference between code that runs in a demo and code that runs in production, and we know which is which.
We don't push a specific LLM. Architecture first, model second. You own the code, so swapping GPT-4o for Claude or Llama after launch doesn't mean rewriting the agent.
Every agent ships with structured outputs, RAG grounding, automated evals, HITL checkpoints, and full audit logs. We won't put an unmonitored agent in front of your customers. Ever.
Gartner predicts 40% of agentic AI projects get cancelled. We use 2-week pilots, fixed-bid scopes, and explicit go/no-go points. If the value isn't there, you kill it cheap. Not after a year of burn.
ISO 27001 and ISO 9001 certified. GDPR and HIPAA aligned. SOC 2 controls and ISO 42001 alignment for AI governance. Documented data handling for regulated workloads. BAA on request.
Most failed agentic AI projects break at integration. Connecting to Salesforce, NetSuite, SAP, HubSpot, ServiceNow, custom APIs: that's a first-class engineering problem for us, not an afterthought handed off to a junior.
Houston-based AI engineering leads with offshore delivery teams under one accountable PM. No 12-hour Slack lag, no offshore-only handoffs, no broken telephone.
Published ranges. No "request a quote" black box. Fixed-bid 1-week discovery and 4–8-week pilots. You know what you're paying before committing deeper.
80+ reviews from real clients, independently verified by Clutch. Recognized by Inc 5000 and listed across multiple "top AI development company" indexes.
What you can verify, not just claims.
8 questions. 90 seconds. Get a tailored readiness score and a custom plan for your team.
We curated the most upvoted concerns from r/AI_Agents, Quora, and Hacker News, and answered them honestly.
Because demos hide compounding errors. An agent that's 85% accurate per step succeeds at a 10-step workflow only 20% of the time. The fix is engineering, not prompting. Structured outputs, schema validation, RAG grounding, automated eval harnesses, and human-in-the-loop checkpoints for high-stakes actions. We engineer for per-step accuracy first. Not for the wow-moment demo.
Gravitee's 2026 report found 3M+ AI agents in production globally. Only 47.1% are monitored or secured. The answer isn't to ban agents, it's to centralize the platform. One observability stack. One set of approved models and frameworks. One audit-log standard. We help clients stand this up alongside the first agent, not after the 50th one shows up unannounced.
Honestly? This is the most underestimated cost in agentic AI projects. Integration engineering and QA often eat 40–60% of total build effort. We staff projects with senior integration engineers, not just ML researchers, and we build the integration layer first. Agent second. The order matters.
Four levers. (1) Smaller models for routing and classification, big models only for the hard call. (2) Prompt caching where supported. (3) Aggressive context-window discipline. Don't dump every doc, retrieve the right chunk. (4) Hard rate-limits and per-user/per-tenant cost telemetry in the observability layer. We instrument cost from day one, so it's never a surprise.
The 12 questions buyers consistently ask before signing a statement of work.
Our senior engineers reply within 24 hours. No sales gate. Just answers.
30 minutes. Senior AI engineer. No pitch deck. We'll either hand you a scoped pilot plan or tell you straight up why an agent isn't the right answer.
