Days 1 to 5
Inside the work
Your engineer joins your standups, gets read access, maps one workflow end to end, and inspects the real data behind it, not the sample set.
Forward-Deployed Engineering
Hire a forward-deployed AI engineer, embedded with your team from week one
Codiste engineers work inside your systems, permissions and release process to take AI from prototype to production. Start with a two-week free trial: no fee, no contract, keep whatever we build.
Why do you need a forward-deployed engineer?
Unlike vendors who work from a requirements document, an FDE starts at your desk, watching how your team actually works. The solution fits your real workflow from day one.
Instead of passing work between scoping, coding, and deployment teams, one engineer builds it, deploys it, and connects it to the systems you already use. Nothing gets lost in handoffs.
While most projects end with a handover call and unread documents, an FDE walks your team through what was built, what to watch for, and how to report issues clearly.
How the 2-week free trial works
You do not need to sign anything to find out whether this works. An engineer joins your team for two weeks, builds against your real systems, and demos on day 10. Then you decide.
Days 6 to 9
A working slice
They build a functioning slice of that workflow against your actual systems, with the permissions and failure paths it will meet in production.
Day
10
Demo and decision
You see it run. Continue into a scoped engagement, or stop there. No notice period, no invoice, no conversation you have to get out of.
You keep everything
The scoping document, the baseline measurements, and all code written during the two weeks stay with you. The IP was yours from day one.
What we need from you to start
- A signed NDA
- Read access to one system
- One person who knows the process
- About 2 hours a week of their time
Three ways to work with us
Every engagement starts with the same free two weeks. What follows depends on how much is already built and how long you need the capacity.
2 weeksDiscovery Sprint
We map one workflow, inspect your data, and come back with a scoped build plus a working slice you can run.
- Best for
- Teams who know AI should help, but not where to point it
- You get
- Scoped workflow, baseline, acceptance criteria, working slice
- Price
- Free, no contract
4 to 8 weeksPrototype → Production
One AI workflow taken from a demo that works on clean data to a live release inside your systems.
- Best for
- Teams with a working prototype and no path to production
- You get
- One workflow live, with evaluations, permissions and monitoring
- Price
- Custom, fixed scope
3, 6 or 12 monthsEmbedded AI Engineer
A forward-deployed engineer inside your team, your repo and your standups. Continuous capacity for whatever ships next.
- Best for
- Products with an ongoing AI roadmap and a loaded backlog
- You get
- Dedicated engineer plus your team trained on the delivery method
- Price
- Custom, monthly
Codiste FDE vs. the alternatives
Four other ways to get AI into production, and where each one costs you time, ownership or control.
- How it starts
- 2-week free trial
- Working software in your environment
- Week 1
- Who scopes the work
- Engineer with your team, continuously
- Evaluations, permissions, monitoring
- Built in
- Pricing
- Custom, fixed scope per phase
- You own the code and docs
- Yes, fully
- Exit
- Documented handover
- How it starts
- Spec and proposal
- Working software in your environment
- 2 to 3 months
- Who scopes the work
- Fixed spec up front
- Evaluations, permissions, monitoring
- Often out of scope
- Pricing
- Time and materials, creep risk
- You own the code and docs
- Usually
- Exit
- Support contract
- How it starts
- CV shortlist
- Working software in your environment
- 2 to 4 weeks onboarding
- Who scopes the work
- You do
- Evaluations, permissions, monitoring
- Depends on the person
- Pricing
- Monthly rate
- You own the code and docs
- Yes
- Exit
- Ends, context lost
- How it starts
- 3 to 6 month hiring loop
- Working software in your environment
- 3 to 6 months
- Who scopes the work
- You do
- Evaluations, permissions, monitoring
- Yours to build
- Pricing
- Salary plus overhead
- You own the code and docs
- Yes
- Exit
- Not applicable
- How it starts
- A subscription
- Working software in your environment
- Days, then stuck
- Who scopes the work
- You do
- Evaluations, permissions, monitoring
- Not covered
- Pricing
- Per-seat subscription
- You own the code and docs
- No, locked to the tool
- Exit
- Migration project
What you get, and what you own
Handover is not a final email. These are the deliverables named in the engagement, and they belong to you whether we continue past the trial or not.
Repository and full commit history
Your organisation, from the first commit
Evaluation suite
The test cases we judged quality against
Monitoring and alerting
Wired to your stack, not ours
Deployment runbook
Release, rollback and recovery steps
Architecture and troubleshooting docs
Written for your engineers, not for us
Two handover sessions
Live, recorded, with your team
Working with us, on the security side
- NDA signed before any access is granted.
- IP assigned to you on day one.
- Least-privilege access by default, scoped to the systems the workflow actually touches.
- No production credentials without your written approval.
- Your data is never used to train models.
What is a forward-deployed engineer?
A forward-deployed engineer is a software engineer who works directly with a client’s team to understand a business problem, build a solution, and deploy it in the client’s operating environment.
FDE stands for forward-deployed engineer. The role combines hands-on engineering with close involvement in how the software is used.
In the FDE model, discovery continues as the engineer builds. User feedback, system limitations, and production behaviour inform the next decision.
Codiste applies Forward-Deployed Engineering to AI products and workflows. Your embedded AI engineer works with your technical and business teams to connect model behaviour with the integrations, controls, and user experience the application needs.
Codiste’s forward-deployed engineering capabilities
04
Voice AI and conversational AI development
Our forward-deployed engineering tech stack
Technology earns its place by helping the system perform. We assess the quality, response time, running cost, and data requirements of your use case before recommending the architecture. We build around your existing environment and select additional tools where the workflow requires them.
LangChain
LangGraph
LlamaIndex
CrewAI
OpenAI Agents SDK
Claude Agent SDK
MCP
The engagement, week by week
The first two weeks are the free trial. Everything after it is scoped from what those two weeks actually found.
Weeks 1 to 2 · Free
Understand the work
We speak with the people doing the job and review the systems supporting it. Together, we identify the bottleneck, establish a baseline, and agree what improvement would make the engagement worthwhile.
Weeks 3 to 6
Build alongside your team
Your engineer works with the relevant stakeholders and follows the agreed development and review process. Regular demonstrations make progress visible and bring real feedback into the build while changes are still inexpensive.
Weeks 5 to 8
Test the whole workflow
We evaluate representative tasks, difficult inputs, permissions, and failure paths. Quality matters alongside response time, cost per completed task, and the amount of human intervention the workflow still needs.
Week 8 onward
Release and transfer ownership
We plan a controlled rollout, review behaviour in use, and address the issues it reveals. Documentation and knowledge transfer cover deployment, troubleshooting, and future changes, with ongoing support defined in the engagement.
See what your AI feature needs to reach production
Identify which workflow to build first and how it will connect with the systems you already run. Discuss your data access, permissions, and project scope with Codiste.
How does forward-deployed engineering work?
A useful AI application needs a deliberate path from a user’s request to a system’s response. For workflows that take actions, we design that path around explicit permissions and validation. The four controls beneath the path apply at every stage of it.
The request path
Request
A user asks, from inside your product, with their identity attached.
Permissions
What they may see and do is resolved before any model runs.
Approved context
Only the records they are cleared for reach the model.
AI workflow
The model reasons over that context and proposes an action.
Validated result
Application logic checks the proposal, then the system acts.
Access control by user and task
Connecting a data source does not give every user unrestricted access to its contents.
Business rules check every AI action
Consequential actions can require explicit confirmation or review before execution.
Error handling and human escalation
Missing information, failures and uncertain results get a response the user can understand.
Logging and monitoring for your team
Your team can trace errors and investigate changes in behaviour from the records kept.
AI projects where forward-deployed engineers fit best
AI features for an existing product
Add an assistant, document workflow, or conversational interface to an existing product. Your FDE connects the AI behaviour to authentication, product data, and the experience customers already know.
AI for customer support operations
Give support teams a workflow that retrieves relevant context, prepares responses, and performs approved actions. Measure resolution time and escalation quality alongside answer accuracy.
AI lead qualification and sales follow-up
Connect lead intake with qualification, scheduling, and CRM updates. Design around current availability, consent requirements, and the point at which a salesperson should take over.
AI document processing
Extract agreed fields, validate them against business rules, and route exceptions for review. Track processing time, corrections, and the proportion of documents that still need manual work.
AI engineering for venture studio portfolios
Bring engineering support into portfolio companies with different products and constraints. Establish shared delivery practices while keeping each company’s data, permissions, and product decisions separate.
Industries our forward-deployed engineers work in
Every industry carries its own systems, permissions and rules.
Our engineers work inside them, so what gets built fits the way the business already runs.
Proof from shipped work
Codiste’s portfolio shows how AI capabilities connect with applications, live data, and operational controls.
Why teams choose Codiste
Choosing a forward-deployed engineering agency means choosing the people who will make technical decisions close to your business. You need to understand how they work, what they can deliver, and how your team will judge the result.
Shipped AI work across voice, CRM, and fintech
Our published work spans voice interfaces, CRM workflows, financial applications, and personalised content. That experience matters when a feature depends on several systems working together.
Acceptance criteria agreed before the build
We define the workflow and its acceptance criteria before expanding the build. Each release has a clear purpose: less manual processing, better enquiry handling, or a capability customers can use.
Full handover of code, docs, and monitoring
Handover covers how the solution is deployed, monitored, and changed. We agree the deliverables, ownership terms, and support so your team can plan what happens after launch.
Get the clarity you deserve
Yes. There is no fee, no contract and no obligation to continue. You cover nothing. We ask for an NDA, read access to one system, and about two hours a week from someone who knows the process. We run the trial because scoping an AI workflow properly takes real access, and a proposal written without it is guesswork.
On day 10 your engineer demos the working slice they built. You then choose to move into a scoped engagement or stop. If you stop, you keep the scoping document, the baseline measurements and all code written during the two weeks. The IP is assigned to you from day one, so nothing has to be handed back.
The cost depends on the engineer’s involvement, project complexity, integrations, and support requirements. Share your workflow and environment with Codiste so we can scope a proposal. During scoping, we discuss engineering fees separately from model usage, cloud infrastructure, software licences, and ongoing support, giving you a view of both delivery cost and running cost.
You do, from the first commit. Code is written into your repository or transferred to it, IP is assigned under the NDA and engagement agreement before access is granted, and the handover includes documentation, the evaluation suite and the deployment runbook. There is no licensing arrangement and no dependency on Codiste to keep the system running.
FDEs are typically involved in implementation and deployment as well as discovery. Consulting engagements can include those responsibilities too, so compare the actual scope. A deployment strategist often focuses on identifying opportunities and coordinating adoption; the FDE takes a hands-on engineering role in delivering the solution. Titles vary between organisations.
Staff augmentation usually adds capacity within your existing management structure. An FDE engagement typically includes deeper involvement in problem definition, stakeholder feedback, and delivery through deployment. A contractor can also work this way. The useful distinction is the responsibility agreed, including who scopes the work, approves it, and supports it.
An FDE can suit a defined delivery need, a specialist capability gap, or a period when the scope is still taking shape. A full-time hire makes sense when you have sustained work and need permanent ownership inside the business. The two can work together, with an FDE helping deliver a release and transfer context to your internal team.
Hire a forward-deployed engineer for your next AI release
Two weeks, no fee, no contract. If it is not working by day 10, you keep the scoping document and we part ways.
Stop paying for a prototype that never reaches production
Every month an AI feature sits in demo, your team keeps doing the work by hand. An embedded engineer works inside your systems, your permissions, and your release process until the workflow runs without them.