AI agents that do the work, not the demo.
We design, build, and deploy agentic systems that run real business processes end to end — engineered for production, owned by you.
- 01
Assess
- 02
Pilot
- 03
Build
- 04
Deploy
- 05
Operate / Hand off
Built on the tools serious teams trust
Four ways we take work off your team.
From a single workflow to a self-hosted model stack — each one owned and operated by you.
We start with the process, not the model.
Scope a real workflow, build it in your stack, prove it with evals, then hand it over. The five steps below are the whole engagement.
- 01
Assess
We map one real process end to end and find where agents beat both humans and rigid RPA.
- 02
Pilot
We build a bounded pilot in your stack and run it in shadow mode against real data.
- 03
Build
We harden it: guardrails, evals, tracing, fallbacks, and idempotency before any write access.
- 04
Deploy
We ship to production with monitoring, audit logs, and scoped permissions on every action.
- 05
Operate / Hand off
We document the stack and hand it to your team — no black boxes, no lock-in.
Every step is observable, testable, and built to fail safely.
Rule-based automation tops out near 70% of a process. Agent reasoning on a deterministic scaffold pushes that past 90% — without giving up control.
- Mapped end to end, then automated step by step — tool calls, retrieval, human checkpoints.
- Connects to your CRMs, ticketing, databases, and internal APIs.
- Built-in evals and tracing on every run.
- Guardrails and fallbacks at each step, so a failed call never becomes a silent error.
- Intakeclassify & validate the request
- Plandecompose into discrete steps
- Tool callsCRMs, ticketing, DBs, internal APIs
- Validateevals, schema & guardrail checks
- Human approvalcheckpoint before any write
- Deliverwrite back + audit log
Swap a hosted API for a model that runs in your VPC.
Same SDK, same code — pointed at an open model you own and operate. Sensitive data never leaves your environment.
- Open models served on your hardware or VPC — no data leaves your network.
- vLLM for high-throughput production; Ollama and llama.cpp for single-node and edge.
- OpenAI-compatible endpoint — existing code switches with a one-line change.
- Supports GDPR, HIPAA, and SOC 2 by design.
from openai import OpenAI
# point the same SDK at your self-hosted vLLM
client = OpenAI(
base_url="https://llm.internal.vpc/v1",
api_key=os.environ["ZYNAPSE_KEY"],
)
resp = client.chat.completions.create(
model="hermes-4-70b",
messages=msgs,
)
Use cases, the way we’d actually build them.
Same shape every time: the problem, exactly what the agent does, the systems it touches, and an honest outcome. Expand any to see the pipeline.
Clinical and admin staff burn hours in payer phone and fax queues per request, and patients wait days while approvals stall.
What the agent does
- 01Detects the prior-auth-triggering order in the EHR and looks up that payer’s medical-necessity rules.
- 02Pulls the supporting evidence from the chart — diagnosis codes, prior treatments, relevant notes.
- 03Pre-populates and submits through the payer portal, then chases status until a decision returns.
- 04Writes the approval number back into the EHR and notifies the care team.
- 05Routes denials and edge cases to a human, who always makes the clinical call.
Systems it touches
Outcome
Days → same-day · up to ~60% less admin phone time
Representative — vendor / industry-reported, not an audited average.
Compliance teams spend most of the day on low-value transaction-monitoring alerts and hand-writing Suspicious Activity Reports, while genuine high-risk cases wait.
What the agent does
- 01Pulls the flagged transaction, customer profile, and prior alert history into one case file.
- 02Enriches it with KYC data, sanctions / PEP screening, and behavioural signals.
- 03Scores the risk and decides whether to clear the alert or escalate it.
- 04Drafts the SAR / STR narrative from the assembled evidence.
- 05Routes every high-risk case to a human investigator for sign-off, and logs each decision.
Systems it touches
Outcome
~70% less SAR preparation time
Illustrative single deployment — not an industry benchmark.
Lawyers spend hours on first-pass review of NDAs, MSAs, and vendor agreements — a bottleneck that pushes overflow to expensive outside counsel.
What the agent does
- 01Ingests the counterparty contract and identifies clause types and any missing provisions.
- 02Checks every clause against your company playbook and risk thresholds.
- 03Flags problem terms — liability, indemnity, termination, data — and proposes redlines.
- 04Generates a plain-language issues report ranked by risk.
- 05Hands the marked-up document to the attorney and tracks the negotiated version.
Systems it touches
Outcome
2–3 hrs → ~10–15 min · ~14 hrs/wk saved per lawyer
Self-reported vendor survey of 100+ in-house teams — directional.
After a catastrophe event, claim queues spike and even simple, low-value claims take days of manual handling — frustrating policyholders exactly when they need a fast payout.
What the agent does
- 01Parses the first-notice-of-loss and any photos into a structured claim record.
- 02Verifies the policy actually covers the loss against limits and exclusions.
- 03Confirms a matching external event (weather / outage) and runs fraud-signal checks.
- 04Calculates the settlement and assembles a cited, auditable summary.
- 05Escalates anything risky or ambiguous; a claims professional makes the final call.
Systems it touches
Outcome
~80% less processing time on in-scope claims
Allianz “Project Nemo”, company-reported, scoped to a narrow claim type.
High-volume support across many markets and languages is expensive and slow to staff around the clock, and long resolution times erode CSAT.
What the agent does
- 01Authenticates the customer and pulls their order and payment context from the OMS.
- 02Reads the intent and retrieves the relevant policy and knowledge.
- 03Takes the action — refund, payment escalation, order status — in the customer’s language.
- 04Confirms resolution with the customer.
- 05Escalates genuinely complex or sensitive cases to a human with full context attached.
Systems it touches
Outcome
~2/3 of chats handled · 11 min → under 2 min
Klarna early-deployment milestone — later re-added humans for complex cases.
3PLs and shippers process hundreds of routine ops emails a day and quietly lose money to overbilling, suboptimal routing, and missed delivery windows.
What the agent does
- 01Reads and classifies each inbound order or exception email and extracts the shipment details.
- 02Selects and optimizes the carrier and route against cost and service constraints.
- 03Pushes instructions and tracking into the TMS and notifies the stakeholders.
- 04Monitors real-time status and flags delays before an SLA is breached.
- 05Audits the freight invoice against the booked rate to catch overbilling.
Systems it touches
Outcome
~25% lift in on-time-in-full · route/cost optimization up to ~25%
Vendor case-study figures and an industry range — representative.
A small team is stuck: a legacy migration has stalled, reviews are slow, coverage is thin, and they can’t send proprietary source to a third-party API for every routine agent loop.
What the agent does
- 01Configures Claude Code with a tuned CLAUDE.md, repo slash-commands, and purpose-built subagents.
- 02Runs CI where an agent auto-generates tests and does first-pass PR review with cited diffs.
- 03Routes sensitive or high-volume loops to a self-hosted Nous Hermes model behind the same interface.
- 04Measures impact with an eval harness — PR velocity and tech-debt, not lines of code.
- 05Keeps a human gate on every merge.
Systems it touches
Outcome
~56% faster on a controlled coding task (95% CI 21–89%)
Controlled study + vendor-engineering figures — expect an initial adjustment dip.
Compliance (HIPAA / GDPR / SOC 2) forbids sending PHI or PII to an external LLM API, and at this query volume per-token pricing is the most expensive option on the table.
What the agent does
- 01Serves an open model (e.g. Nous Hermes) on your GPUs via vLLM with continuous batching.
- 02Exposes an OpenAI-compatible gateway so existing app code drops in unchanged.
- 03Adds RAG retrieval plus structured-output and function-calling over internal data.
- 04Layers in autoscaling, observability, and an eval / guardrail layer.
- 05Right-sizes hardware to the latency budget and documents the cost breakeven vs API.
Systems it touches
Outcome
~3.2× vLLM throughput · ~40–80% lower cost above breakeven · 0% data egress
Practitioner cost-modeling — below breakeven volume, GPU-ops time can erase savings.
Every figure is labelled by source — company-reported, vendor case study, or industry estimate — and never invented. Gartner expects 40%+ of agentic projects to be cancelled by 2027; we build the kind that ships.
Don't see your process? Tell us about itOutcomes we can stand behind.
Representative results from production agent systems. Client work is anonymized; the numbers are the kind we measure and report.
Built by us · Agent Workflow
Zynapse Meet
An AI meeting agent we designed, built, and shipped end to end — a working example of the Agent Workflow muscle we sell: real-time transcription, summarization, and searchable knowledge reuse.
Visit Zynapse MeetResults
- Transcription accuracy
- 98%
- Summary latency
- 30s
- Time saved / user
- 5+ hrs/wk
Financial services · Automation
Alert-triage agent for a mid-market bank
An autonomous triage agent that screens KYC and sanctions alerts, clears low-risk cases, and escalates the rest with a full audit trail. Delivered under NDA.
- 2–3× investigation throughput
- Every decision logged
Logistics · Automation
Track-and-trace agent for a freight operator
An always-on agent that monitors in-transit loads, updates the TMS, and notifies customers — replacing manual status checks across the ops desk. Delivered under NDA.
- ~1 hr/day saved per ops rep
- Reroutes on SLA breach
You stay in control.
Your data stays in your environment, your team can run what we build, and every agent action is logged, scoped, and reviewable. No black boxes, no lock-in.
Notes from the team.
Field notes on shipping agents, MCP, RAG, and self-hosted models.
May 22, 2026 · 5 min
The Agent You're Building Will Probably Fail — and That's a Process Problem
Most agent projects fail for process reasons, not model ones. Six steps — ground truth, workflow redesign, evals before deployment, explicit human oversight, token-cost design, and change management — matter far more than the model, which is maybe 10% of whether the agent succeeds.
February 22, 2026 · 12 min
Agent Skills
Agent Skills package procedural knowledge into reusable folders — instructions, rules, templates, and optional scripts — that agents detect and apply automatically. The result: portable, composable, consistent workflows across platforms.
August 23, 2025 · 7 min
Why "The Lean Startup" Matters Today
Eric Ries' Lean Startup, distilled into 12 actionable principles across Vision, Steer, and Accelerate. Why validated learning and the Build–Measure–Learn loop still beat building in the dark — with examples from Dropbox, Tesla, and Airbnb.
Tell us the process. We'll show you the agent.
Bring one repetitive workflow that costs your team hours every week. We'll scope what an agent can take off your plate — and what it'd take to ship it.