Workflow discovery
We sit with your team, map the work as it actually happens, and find the highest-leverage gaps.
Service · Forward-deployed100× faster delivery
Forward-deployed engineers building
agentic AI for real business problems
We don't drop a deck and disappear. We embed senior engineers inside your business, walk the floor, find the gaps where work breaks down, and ship production agents that actually solve the problem - with evals, guardrails, and audit trails baked in.
DaysFrom spec to first agent in prod96.4%Eval pass rate across deployments
Overview
Most agent demos die in production. We engineer for the day after launch.
The gap between an impressive Loom demo and a regulated production system is where 90% of AI projects die. They lack evals, hallucinate at the wrong moment, leak data, or simply fall over under real load.
We're forward-deployed engineers. We sit inside your team, learn the workflow, and ship agentic systems that survive the messiness of real enterprise: regulated processes, audit trails, vendor lock-in, multi-tenant data, and budget guardrails included.
- Embedded deliverySenior engineers inside your team - in your Slack, your codebase, your standups - until it ships.
- Multi-agent orchestrationSpecialist agents that delegate, debate, and hand off through structured workflows.
- Guardrails by defaultPolicy, PII, prompt-injection, and tool-use guardrails layered into every agent.
- Continuous evalsGolden sets, regression suites, and live-traffic shadowing on every release.
Capabilities
Everything you need to ship agents that ship value
End-to-end agentic AI engineering - from forward-deployed discovery through regulated production deployment.
Agent design
Pattern selection, tool design, memory model, and policy layer for each agent.
Knowledge & RAG
Vector indexes, hybrid retrieval, and grounding pipelines tuned for your data.
Evaluation
LLM-as-judge, golden sets, regression suites, and live shadow testing.
Safety & guardrails
PII detection, prompt-injection defense, output policy, and human-in-loop.
Deployment
Self-hosted, hybrid, or fully managed on AWS, Azure, or GCP.
Observability
Tracing, metrics, cost, and quality dashboards built into the runtime.
Operations
On-call, SLOs, and continuous improvement loops once your agents are live.
Outcomes
Numbers that move the business
Days
From spec to first agent in prod
96.4%
Eval pass rate across deployments
60%
Lower cost vs in-house build
300+
Agents shipped to production
How we work
Embed → Map → Build → Operate
A predictable, evals-first delivery model that compounds across every agent you ship.
- Phase 01
Embed
Senior engineers join your standups in week one. We learn the work the way the team actually does it.
- Phase 02
Map
We surface the gaps, size opportunity, and pick the agent that pays for the engagement first.
- Phase 03
Build
Agent design, tools, RAG, and guardrails wired into a working system with eval harness in place.
- Phase 04
Operate
Roll out under SLOs with on-call, incident response, and continuous evals. We don't ghost.
FAQs
Frequently asked questions
Everything decision-makers and engineers ask before kicking off.
Our engineers are embedded inside your team - in your Slack, your codebase, your standups, your office where it makes sense. We don't write a spec from a Zoom call and disappear. We learn the workflow as it actually runs, find the gap, and ship into your environment under your review.
Chatbots answer questions. Copilots assist a user. Agentic systems take actions on behalf of the user - looking up data, calling tools, making decisions, and handing off across specialist agents. They require deeper engineering: tool contracts, guardrails, memory, evaluation, and observability.
Yes. We build provider-agnostic agentic systems that run on OpenAI, Anthropic, AWS Bedrock, Google Gemini, or open-weight models on your infrastructure. We help you choose the right model per task and design for portability.
We layer multiple controls - grounded retrieval, tool-driven verification, structured outputs, policy guards, and continuous evaluations on golden sets. Critical workflows are wrapped with human-in-the-loop review and confidence thresholds. We measure hallucination rate and treat it like any other production SLO.
Most engagements run as embedded pods (3-6 months) or fixed-scope sprints (4-6 weeks per agent). We operate on milestones with weekly demos, eval reports, and a clear path to production from week one.
We engineer to the controls those standards require - PII redaction, audit trails, role-based access, model risk reviews, and continuous evidence collection - and we deploy in your environment so your existing certifications cover the engagement. Where you need a specific attestation, we work with your security team to scope it together.
Yes. Most customers retain Techimax pods for managed operations under SLOs - including on-call, regression suites, model upgrades, evals, and continuous improvement. We can also fully transition to your team with knowledge transfer.
A first agent in production typically runs USD 60-120K depending on scope and integrations. A multi-agent enterprise platform runs USD 250K-1M+ for a 6-12 month engagement. We provide fixed-fee proposals after a one-week scoping sprint.
Free working session with a forward-deployed engineer
Show us the problem. We'll send back a written plan to fix it.
In 60 minutes with a senior engineer, you walk away with the gaps mapped, the agent worth building first, a risk read on what your team has already shipped, and a reference architecture - at zero cost, no obligation.
- Workflow map
Where the work breaks down today and which gap an agent should close first - calibrated to your business.
- Velocity diagnostic
Where engineering and ops hours actually go - and where forward-deployed delivery takes you next.
- Risk & rescue read
An honest view of what your team has already vibe-coded and what it needs to survive production.
- Architecture sketch
Reference architecture for your runtime, evals, RAG, and integrations - vendor-agnostic.
60-minute working sessionSenior forward-deployed engineerTwo follow-ups + written summaryFree of charge for qualified businesses
Most teams walk away with a fixed-scope plan in a single session.
Book your free assessment
Reserve a 60-minute working session with a senior AI engineer and practice lead.
Ready to embed an engineer this week?
Tell us the workflow you'd like solved. A senior engineer responds with a fixed-scope plan in 48 hours - including evals, guardrails, and a path to production.