AI Agents · Bespoke Agentic Development

Custom agents engineered for your workflow, your data, your guardrails.

Off-the-shelf agents won't fit the workflows that actually matter inside your enterprise. We design, build, and deploy bespoke agents — with the engineering discipline of production software, not demo-ware.

Lifecycle

Six phases.
Eval-first throughout.

01

Discovery

Map the workflow, the data sources, the success metrics, the regulatory boundaries, and the human-in-the-loop checkpoints.

02

Architecture

Choose models, tools, retrieval strategy, memory, orchestration framework. Document the agent contract before code.

03

Build

Implement with eval-first discipline. No prompt change ships without measurement against the eval set.

04

Evaluate

Accuracy, latency, cost, fairness, robustness, drift. Eval harness shipped alongside the agent — not as an afterthought.

05

Deploy

Production hardening: rate limits, tool guardrails, audit logging, monitoring, escalation paths, rollback plan.

06

Operate

Continuous eval against new data, prompt and tool refinement, capability expansion — co-piloted with your team or fully managed.

Stack

The toolkit we ship from.

01

Foundation models

Claude (Anthropic), GPT (OpenAI), Gemini (Google), open-source (Llama, Mistral, Qwen). Picked per workload.

02

Orchestration

LangGraph, CrewAI, AutoGen, or custom — whichever fits the workflow shape and team's ops maturity.

03

Tooling protocol

Model Context Protocol (MCP), function calling, custom tool servers. Tool design is half the engineering.

04

Retrieval

Hybrid (BM25 + dense), reranking, contextual retrieval. Vector DBs: pgvector, Qdrant, Pinecone, Weaviate.

05

Memory

Episodic, semantic, working — designed per use case. We don't bolt on memory because it's fashionable.

06

Evaluation

Custom eval harnesses with golden sets, LLM-as-judge, and human-rater workflows. Drift detection built in.

07

Observability

LangSmith, Langfuse, Phoenix, or your existing APM. Every prompt, tool call, and decision is traceable.

08

Deployment

Your VPC, on-prem, or our managed cloud. Models on-host where data residency demands it.

Deliverables

What you get
at handoff.

01

The agent

Production code, in your repo, in your stack. No black boxes, no licensed runtime.

02

The eval harness

A test suite that says "is this agent better than yesterday's version" — with regression detection.

03

The runbook

On-call runbooks, escalation paths, prompt-update procedures, eval-drift response.

In the field

What bespoke agents
ship in practice.

CASE 01

IT services firm, RFP response agent

Context

A services firm answered 300+ RFPs per year. Each response took 60+ hours across SMEs. They wanted to compress that cycle without diluting quality.

Outcome

Bespoke RFP agent that pulls from a knowledge base of prior responses, drafts section-by-section, and assigns SME reviews where confidence is low. Cut first-draft time by 75%; SMEs now edit, not author.

CASE 02

Manufacturing firm, supplier-doc analysis

Context

A supplier qualification process required reviewing 80+ pages of documentation per supplier across financials, compliance, technical capability. 6+ hours per file.

Outcome

Agent that extracts, classifies, and scores supplier docs against the firm's qualification rubric. Cut review time to 45 minutes per supplier with a human approval at each scored dimension.

Let's build

Have a workflow
you've been wanting to automate?

Send us the workflow. We'll come back with an architecture sketch and a build plan.