AI agents that actually ship to production.
Autonomous workflows, 24/7 execution.
Demos are easy. Production is the hard part. We build evaluated, observable, guardrail-protected agents that handle real work — support, sales, ops — without embarrassing you on the output.
Outcomes that matter
Problems We Solve
What's actually broken — and how we fix it.
Most AI-agent projects get to a wow-demo and stall. The gap between prototype and production is where 80% of the effort lives. Here's what breaks.
Demos don't survive reality
Weekend prototypes hallucinate, leak PII, and can't handle the edge cases real users bring. No eval, no safety net.
Prompt injection goes unnoticed
Users (or attackers) override instructions, leak prompts, or exfiltrate data. Without layered defenses, your agent is a liability.
Zero observability
When something goes wrong you have no trace — no prompts, no tool calls, no versioning. You can't fix what you can't see.
Tool use fails silently
Agents hit rate limits, misuse APIs, or call the wrong tool — and the user gets a confident-sounding wrong answer.
Cost spirals without controls
A chatty agent on a big model with a loose loop burns $8k in tokens overnight. No budget guards, no model routing.
No evaluation harness
Vibes-based testing doesn't survive a real change. Without a regression suite and LLM-as-judge, every tweak is a gamble.
Features & Capabilities
Every module, shipped tuned to your data.
The features below are what separate 'someone wired up GPT' from 'production-grade autonomous system.'
Agent Capabilities
Retrieval-augmented (RAG)
Hybrid keyword + semantic retrieval, re-ranking, chunking strategies tuned to your corpus. Freshness indexing for live data.
Multi-step tool use
Agents that plan, call tools, observe results, and replan. With error recovery, not just a straight-through pipeline.
Voice + chat + email
Same underlying agent, multiple surfaces. Streaming speech, real-time interruption handling, unified conversation memory.
Memory & long-running context
Short-term conversation memory, long-term user facts, and episodic memory for recurring customers.
Orchestration
Graph-based workflows
LangGraph / Temporal-backed execution with branching, retries, and deterministic replay for debugging.
Human-in-the-loop
Escalation rules, approval gates, and 'agent asks human' flows — with SLA tracking and handoff transcripts.
Fallback + degradation
If the primary model / tool fails, fallbacks engage automatically. Degrade gracefully rather than break.
Scheduling & async jobs
Long-running agents run on queues, not request threads. Durable state, resumability, and priority queues.
Observability & Evaluation
Full trace capture
Every prompt, tool call, model response, and cost recorded. Searchable, replayable, exportable.
LLM-as-judge eval harness
Regression suites run before every deploy. Pass / fail thresholds gate production pushes.
Production A/B tests
Route a slice of traffic to a new prompt / model / tool config. Compare quality + cost + latency in real time.
User feedback capture
Thumbs up/down, free-text, and conversation repair flows feed back into the eval dataset.
Safety & Guardrails
Prompt-injection defense
Layered defenses: input sanitization, output filtering, tool allowlisting, and red-team test cases.
PII + secrets redaction
Redact before it reaches the model. Auditable logs with configurable masking per data class.
Content policies
Domain-specific refusal rules, jailbreak detection, and a configurable moderation layer.
Rate + budget limits
Per-user, per-tenant, per-session spend caps. Hard-stop on anomalous cost spikes.
Model Routing
Claude / GPT / open-source
Pick the best model per task, not per project. Route cheap queries to fast models, complex ones to frontier models.
Semantic caching
Cache semantically-equivalent queries. Typical 30–60% cost reduction on repetitive support traffic.
Cost-aware fallbacks
Fall back to a cheaper model if the primary times out or throws. Quality-guarded degradation.
Fine-tuned routing
Use fine-tunes where they beat prompting. Benchmark, compare, swap in seamlessly.
Deployment
Self-hosted or managed
On your cloud, on ours, or fully managed. VPC peering, BYOK, and data-residency options.
SSO + RBAC
SAML / OIDC, role-based controls, and audit logs ready for SOC 2 / ISO 27001 reviews.
Blue-green deploys
Zero-downtime prompt + model + tool rollouts. Instant rollback on eval regression.
Live shadow mode
Run the agent alongside humans, compare outputs, and graduate to full autonomy on your timeline.
Implementation Approach
From kickoff to production, transparently.
We lead with evaluation, not with the model. Typical pilot-to-production timeline: 8–16 weeks.
Use-case Scoping
Pick the highest-leverage agent for your org. Define success metrics, guardrails, escalation rules, and failure modes.
Deliverables
- Use-case + success metrics doc
- Risk register + guardrail spec
- Golden dataset seed (50+ examples)
Evaluation Harness
Build the test suite before the agent. LLM-as-judge criteria, regression cases, and failure-mode probes.
Deliverables
- Automated eval pipeline
- Baseline scores (accuracy, safety, cost)
- CI gate for prompt / model changes
Agent Prototype
Build, iterate, evaluate — tight loop. Weekly scored demos against the eval harness, not against gut feel.
Deliverables
- Working agent on staging
- Trace viewer for prompt debugging
- Weekly eval score improvements
Tool & System Integration
Wire the agent into your stack: CRM, ticket system, knowledge base, email, voice. Idempotent, recoverable, observable.
Deliverables
- All required tools connected
- Human-handoff flow live
- SLA + escalation rules in prod
Production Hardening
Red-team it. Cost-test it. Load-test it. Sign off on prompt-injection, PII, and jailbreak resistance before launch.
Deliverables
- Red-team report
- Cost + latency SLO acceptance
- Blue-green deployment pipeline
Monitoring & Iteration
Weekly regression runs, monthly eval set refresh, quarterly model-routing review. The agent gets better, not worse.
Deliverables
- Weekly production eval reports
- Monthly cost-optimization pass
- Quarterly capability expansion
Integrations
Plays well with your existing stack.
Model-agnostic, framework-agnostic, cloud-agnostic. We pick the right tool for the job.
Foundation Models
Orchestration
Vector DBs & Search
Voice & Telephony
Knowledge Sources
Observability
Missing something? We build custom connectors in 1–2 weeks.
Use Cases
Ways teams put this to work.
Customer Support Concierge
Tier-1 support agent that resolves account, billing, and how-to queries across chat, email, and voice with live CRM lookup.
Outbound Sales SDR
Multi-channel SDR that researches accounts, personalizes sequences, handles objections, and books meetings.
Document Processing
Structured extraction from invoices, contracts, and KYC forms with confidence scoring and human review queue.
Internal Analyst Copilot
Chat-with-your-data agent answering 'how are we doing on X' from warehouse tables, with safe SQL and explainable charts.
IT Helpdesk
L1 IT agent for password resets, access requests, and common incident triage — with ServiceNow / Jira integration.
Voice Scheduling Agent
Inbound voice agent for appointment booking, rescheduling, and reminders — with realtime calendar + CRM writes.
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