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SearchSynth.
AI Automation & Agent Engineering

Most AI pilots die in the demo. We ship the ones that run unattended.

Voice agents that answer the phone, agentic workflows that close the loop across your CRM and ERP, MCP servers that make your internal systems usable by ChatGPT and Claude — each one shipped with an eval suite, a guardrail layer and a dashboard, because that is the difference between a demo and a system your ops team trusts.

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ONE RUNtriggeragentplan · actMCPCRMDOCSevalsshiphuman

anatomy of one agent run — the eval gate is the part most builds skip

Demo to production pilot
6–10 wksDemo to production pilot
Manual touches removed per workflow
40–70%Manual touches removed per workflow
Builds ship with an eval suite
100%Builds ship with an eval suite
Typical payback on first workflow
< 90 daysTypical payback on first workflow

What we build

Nine capabilities, one engineering standard. Every build is version-controlled, evaluated against a golden dataset before launch, and instrumented after it.

Highest demand

AI voice agents

Inbound and outbound phone agents that qualify, book, chase and escalate — under a second of latency, with a warm human handoff the moment intent gets complex or a caller asks for a person.

  • Telephony, IVR replacement and after-hours coverage
  • Barge-in, interruption handling and multilingual voices
  • Live CRM writes and calendar booking mid-call
  • Full transcripts, recordings and QA scoring on every call

Agentic workflow orchestration

Multi-step processes with real decision points — read, judge, act, verify, retry, escalate — running on durable execution so a failed API call at step nine doesn't silently drop the work.

  • Durable, replayable runs with idempotent side effects
  • Human-in-the-loop approval gates on irreversible actions
  • Deterministic fallbacks when the model is the wrong tool
New in 2026

Custom MCP servers

We wrap your CRM, warehouse, docs and internal APIs as Model Context Protocol tools, so Claude, ChatGPT and your own agents can use them safely — scoped permissions, audit trail, no copy-paste.

  • Typed tool definitions with per-role scoping
  • Read/write separation and full call auditing

RAG & knowledge layer

Retrieval that cites its sources. Hybrid search with reranking over your real documentation, plus freshness pipelines so answers don't quietly rot three months after launch.

  • Hybrid vector + keyword retrieval with rerankers
  • Answer-level citations and refusal on low confidence
  • Scheduled re-indexing and staleness alerts

AI SDR & revenue operations

Enrichment, intent scoring, routing and follow-up that runs the moment a lead lands — so the AE sees a qualified, researched, correctly-owned record instead of a raw form fill.

  • Firmographic + intent enrichment and ICP scoring
  • Round-robin routing with SLA escalation
  • Ongoing CRM hygiene and dedupe automation

Document intelligence

Intake, extraction and validation for invoices, contracts, claims and onboarding packs — with a review queue where a human confirms anything the model isn't sure about.

  • Schema-constrained extraction with confidence thresholds
  • Exception queues instead of silent bad data

Support deflection agents

Tier-one resolution across chat, email and help centre, grounded in your actual policies — measured on resolution rate and CSAT, not on how many tickets it touched.

  • Grounded answers with escalation triggers
  • Ticket triage, tagging and routing

Content & programmatic pipelines

Editorial pipelines that generate from your own data and research, then gate on fact-checks, brand voice and internal-link rules before anything reaches a CMS. Built to survive the spam-policy filters that flattened bulk AI content.

  • Brief generation from live SERP and citation data
  • Fact-check and originality gates before publish
Why builds survive

Evals, guardrails & observability

The part most agencies skip. Golden datasets and regression suites that run on every prompt change, PII redaction and action allow-lists at the boundary, and tracing that tells you exactly what an agent did, what it cost, and where it went wrong.

  • Golden datasets and CI regression runs per release
  • Prompt and model versioning with instant rollback
  • Drift, cost and latency monitoring with alerting
  • Redaction, action allow-lists and spend caps

What lands in your account

  • Opportunity map & ROI model

    Every candidate process scored on volume, error cost and automation feasibility, with the arithmetic behind the business case shown.

  • Architecture and data-flow docs

    System diagrams, tool contracts, model routing decisions and the failure modes we designed around.

  • The build, in your repos

    Workflows, agents, prompts and MCP servers in version control under your org. No black boxes on our infrastructure.

  • Eval suite & golden dataset

    Test cases drawn from your real edge cases, wired into CI so a prompt tweak can't quietly regress accuracy.

  • Observability dashboards

    Runs, resolution rate, escalations, latency and token spend — visible to your team, not just to us.

  • Runbooks & team enablement

    SOPs, escalation paths and live training so your staff can operate and extend the system without us.

The stack we build on

Orchestration

  • n8n
  • Temporal
  • LangGraph
  • Inngest
  • Python

Models

  • Claude
  • GPT
  • Gemini
  • Bedrock
  • Vertex AI

Retrieval

  • pgvector
  • Postgres
  • Elasticsearch
  • Voyage
  • Unstructured

Voice & channels

  • Vapi
  • ElevenLabs
  • Twilio
  • Slack
  • WhatsApp

Evals & tracing

  • Langfuse
  • Braintrust
  • Promptfoo
  • OpenTelemetry

Systems of record

  • HubSpot
  • Salesforce
  • NetSuite
  • Shopify
  • Zendesk
  • Snowflake
Process

How a build runs

  1. Week 1

    Opportunity mapping

    We sit with the people doing the work, time the actual steps, and rank processes by payback. Some come back off the list — automating a broken process just makes it fail faster.

  2. Week 2

    Architecture & eval design

    We define what 'correct' means and build the test set before writing the agent. Success criteria agreed up front, in writing.

  3. Weeks 3–6

    Build & red-team

    Iterative build against the eval suite, then deliberate adversarial testing: prompt injection, hostile inputs, upstream outages, ambiguous handoffs.

  4. Weeks 7–8

    Shadow mode & pilot

    The agent runs alongside your team without authority to act, we compare its decisions against theirs, then widen its permissions as accuracy holds.

  5. Ongoing

    Run, watch, extend

    Model releases and business rules both move. We monitor for drift, re-run evals, cut costs as cheaper models get good enough, and add the next workflow.

Engagement models

Start with the audit if you need a business case, or go straight to a pilot if the process is already obvious. Every engagement can stop at a working artefact.

Automation Blueprint

$9,500

fixed fee · 3 weeks

Process discovery, feasibility assessment and a costed roadmap — enough to take to a budget holder.

  • Process mining across up to 5 workflows
  • Feasibility, risk and data-readiness review
  • Costed roadmap with ROI model
  • Reference architecture and stack recommendation
  • One working proof-of-concept agent

Teams that need a defensible business case first.

Get started
Most chosen

Production Pilot

from$38,000

fixed scope · 8–10 weeks

One high-value workflow taken all the way to production, with evals, guardrails and observability from day one.

  • Everything in the Blueprint
  • Full build of one end-to-end workflow or voice agent
  • Eval suite, guardrails and red-team pass
  • Observability dashboards and alerting
  • Shadow-mode rollout and team training
  • 30 days of post-launch tuning

One painful, high-volume process you already know about.

Get started

Managed Automation Partner

from$6,500

per month · 6-month minimum

We own the automation layer: running what exists, watching it, and shipping the next workflow every quarter.

  • Monitoring, incident response and drift management
  • Continuous eval maintenance and prompt versioning
  • Model migrations and cost optimisation
  • A new workflow or agent each quarter
  • Monthly reporting to business outcomes

Teams running agents in production without an AI engineer.

Get started

Prices are starting points, not quotes. Scope drives the number, and you get a fixed one before you commit.

AI Automation

AI Automation questions, answered

How is this different from an agency that just sells n8n workflows?
Tooling is the easy part. The gap between a workflow that demos well and one that runs unattended is evals, guardrails, observability and error handling — the work that happens after the happy path works. Every build we ship has a golden dataset, a regression suite in CI, spend caps, redaction at the boundary and tracing you can actually read. That is why our builds are still running a year later.
Do we own what you build?
Yes, completely. Code, prompts, workflows, eval sets and infrastructure definitions live in your repositories and your cloud accounts from the first commit. There is no proprietary runtime you have to keep renting from us, and no lock-in clause designed to make leaving expensive.
What does an AI automation project actually cost?
A Blueprint is $9,500 fixed. A production pilot covering one end-to-end workflow starts at $38,000 and typically lands between $38k and $90k depending on integration surface and compliance requirements. Multi-department rollouts run higher. Ongoing management starts at $6,500 per month. That is in line with the wider market, where proofs of concept run roughly $8k–$25k, single-workflow builds $35k–$70k, and production retainers $3k–$15k per month.
Which model do you use?
Whichever one wins the eval for that specific task, at the price and latency the workflow can carry. In practice that means frontier models for judgement-heavy steps and smaller, cheaper models for classification and extraction. We keep model choice behind a routing layer so swapping is a config change, not a rebuild — which matters, because the best option for a given step changes every few months.
How do you stop an agent doing something damaging?
Layered constraints. Agents get explicit action allow-lists rather than open credentials, irreversible operations sit behind human approval gates, spend and rate limits are enforced at the boundary, and inputs are treated as untrusted — we red-team every build for prompt injection before launch. When confidence is low, the agent is designed to escalate rather than guess.
Will this replace our team?
Not in the engagements we take. The work that automates cleanly is the repetitive, high-volume, low-judgement layer — triage, data entry, first-pass drafting, routing, chasing. Our clients typically redeploy that reclaimed time into work that needs a human, and the agent escalates to those humans by design.

You already know which process is broken. Let's cost the fix.

Bring the workflow that eats your team's week. In 30 minutes we'll tell you whether it's automatable, roughly what it costs, and whether it's worth doing at all — including when the answer is no.

30-minute strategy call

With an engineer, not a closer

Book a strategy call

Typical reply time: under 4 business hours.

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