01 / Services
What we do
AI-powered engineering — from CodeForce teams delivering 3–6× productivity to full-stack development and the products we’re building next.
CodeForce · ADLC Delivery
When software learns, the lifecycle has to learn with it.
SDLC was built for code with one job. Agentic systems reason, adapt, and rarely take the same path twice. We deliver them under a different lifecycle entirely — six phases, one feedback loop, every gate accountable to a human.
3–6×
CodeForce engineering throughput vs. baseline, sustained through compliance gates.
4 weeks
Proof-of-Value pilot to first ROI signal — outcomes scoped before sign-off.
The shift, in one frame.
Agentic systems aren’t just faster software. They behave differently — non-deterministic, multi-surface, learning in production. The lifecycle has to match. Here’s where every assumption changes.
| Dimension | SDLC | ADLC |
|---|---|---|
| Behavior | Deterministic. Same input, same output. | Probabilistic. Same input, different paths — by design. |
| Logic lives in | Code and configuration. | Code, prompts, models, tools, and external services — five surfaces, all part of the system. |
| Testing | Pass / fail of known paths. Predefined cases. | Continuous evaluation of reasoning, safety, and tool use against a golden dataset. |
| Success metric | Functional correctness. | Accuracy distribution, hallucination rate, cost per outcome — none collapse to a single boolean. |
| Deployment | End of development. Start of the steady state. | Start of active monitoring. The system keeps changing after it ships. |
| Maintenance | Bug → fix → close ticket. | Drift watch. Behavioral guardrails. Continuous learning loop closes back to discovery. |
| Accountability | Code review. | Human-in-the-loop at every reasoning gate — explicitly mapped before the agent ships. |
Six phases. One feedback loop.
Each phase maps onto an SDLC stage you’d recognize, but the work inside it changes. We’ve codified what we do at every gate so the engagement is legible from day one.
01Discovery & Hypothesis
Before we touch architecture, we map the real workflow — where humans do repetitive judgment work, where the process breaks, and what the cost of failure actually is. Then we form testable hypotheses about where agents earn their keep.
- How it differs from SDLC
Planning used to map features. Here, we map behaviors — because the same inputs can lead the system down different paths.
- How CodeForce delivers this
A two-week discovery sprint led by a Principal Architect. Stakeholder interviews, workflow trace, and a written hypothesis register — each hypothesis tied to a measurable outcome.
- Artifacts you walk away with
Workflow map · Hypothesis register · Ground-truth checklist
Where most teams stall.
We’ve watched four patterns repeat across teams shipping their first serious agentic system. Each one shows up only after production. ADLC is built to make these expensive failures cheap and early.
01 — Skipped
They skip the simulation gate.
Most teams still go spec → build → ship. With agents, the cost of a wrong hypothesis multiplies in production. Simulation against real data is the cheapest place to be wrong; skipping it makes the next six months a recovery.
02 — Untracked
They treat prompts as throwaway.
Prompts are now part of your logic surface — equal weight to code. Without version control, review, and eval, you’re shipping an unobservable codebase that changes silently every time a prompt is tweaked.
03 — Mis-measured
They ship with pass / fail tests.
Functional tests don’t catch what actually breaks agents — hallucination drift, reasoning regressions, cost spikes from a model swap. You need a different test paradigm, instrumented before deployment, not after.
04 — Unwatched
They deploy and walk away.
Models drift. Context windows fill with noise. Tool dependencies change underneath you. Without behavioral monitoring and explicit drift alerts, you find out from your users — and by then the trust loss is already booked.
Three doors into the lifecycle.
Pick where you want us. Full Team Augmentation covers all six phases as an embedded extension of your team. On-Demand Development plugs into specific phases when you need surge. Managed Product means we own the outcome end-to-end.
| Phase | Full Team Augmentation | On-Demand Development | Managed Product |
|---|---|---|---|
| 01 Discovery & Hypothesis | Full | Scoped | Full |
| 02 Scope & Accountability | Full | — | Full |
| 03 Architecture & Eval Design | Full | Scoped | Full |
| 04 Simulation & PoV | Full | Full | Full |
| 05 Implementation & Orchestration | Full | Full | Full |
| 06 Evaluation & Continuous Learning | Full | Scoped | Full |
What we deploy underneath.
The stack is opinionated on purpose. Every layer is here because something fails without it — and most teams discover that after shipping. We bring the answers in.
Orchestration
Agents that coordinate, not collide.
LangGraph · Claude Code agents · Agent supervisors · MCP tool routing
A single orchestration layer manages the agent fleet. You manage the manager, not five concurrent sessions.
Evaluation
Continuous, not on a release schedule.
Golden datasets · RAGAS · Custom rubrics · Regression suites · Hallucination detectors
Eval criteria are written in phase 03, before code. Every change is gated against them before merge.
Observability
Behavioral telemetry, not just uptime.
Drift alerts · Cost-per-outcome · Reasoning trace · User signal capture
Production tells you what eval missed. Signals route back to the next discovery cycle — that’s the loop.
Governance
Auditable. Sandboxed. Yours.
Human-in-the-loop gates · Full audit trail · Sandboxed exec · IP isolation
Client code never trains external models. Every reasoning gate is logged. Every deploy is reversible.
For the CTO doing diligence.
Real questions we hear in week one of evaluation. Plain answers — no marketing softening.
Who owns the IP — including prompts and trained context?
How do you handle hallucinations and rollback?
What does evaluation actually look like in production?
How is this priced — by sprint, by outcome, or by team?
Where do humans actually gate the agents?
How do you keep our code out of training data?
Proof of Value · 4 weeks
Start where the cost of being wrong is lowest.
A four-week Proof of Value pilot. Two senior engineers plus an agent fleet, against a real workflow you pick. Success criteria are signed off before kickoff, scoped to your baseline — and a clean handoff if you stop there.
Most pilots start within two weeks of signed scoping. No long-term contract required.
Engineering Services
End-to-end product development and maintenance — from initial design through production deployment and beyond. We partner with enterprises, ISVs, and startups to build software products, bringing deep expertise across the full technology stack.
01
Discovery
Understand your vision, requirements, and success criteria.
02
Design
Architecture, UX, and technical planning.
03
Development
Agile sprints with continuous delivery.
04
Launch & Scale
Production deployment and ongoing optimization.
Products
Industry-agnostic Agentic AI products, built on the same multi-agent architectures we deliver under CodeForce. Not yet released — contact us for early access.
Early access
Tell us what you’re building and we’ll reach out when a product fits your use case.
Ready to transform your development?
Let's discuss which service is right for your needs.