Sourcegraph proof-of-value plan: what should we test (indexing speed, permissions sync, search relevance, Deep Search quality) in 2–4 weeks?

Most teams only get one short runway to prove out a new code understanding platform. In 2–4 weeks, you need hard evidence that Sourcegraph can handle your scale, respect your permissions model, and actually answer the questions your engineers and AI agents ask every day. That means designing a proof-of-value (POV) that focuses on four things you can measure fast: indexing speed, permissions sync, search relevance, and Deep Search quality.

Below is a practical, time-boxed plan I’d use as a former Staff Engineer / Dev Prod lead running a Sourcegraph evaluation in a hybrid, multi-repo enterprise.

Quick Answer: The best overall choice for a 2–4 week Sourcegraph POV is to design a focused test around indexing speed and coverage. If your priority is security and governance, permissions sync is often a stronger anchor. For AI-assisted workflows and agent readiness, center your POV on search relevance and Deep Search quality.

---

At-a-Glance Comparison

If you only have 2–4 weeks, here’s how I’d rank what to test and how to frame each stream.

Rank	Option	Best For	Primary Strength	Watch Out For
1	Indexing speed & coverage	Proving Sourcegraph can keep up with repo sprawl	Visible, quantifiable wins in days (not months)	Requires realistic sample of large + legacy repos
2	Permissions sync & governance	Security, risk, and platform leaders	Demonstrates alignment with SSO, SCIM, RBAC, and code host ACLs	Needs coordination with identity / security teams
3	Search relevance & Deep Search quality	Developer experience and AI/agent use cases	Shows Deep Search as “agentic AI search” that can answer complex questions with code citations	Needs curated real-world queries and baselines

---

Comparison Criteria

We evaluated each POV focus area against three practical criteria:

• Speed-to-signal: How quickly can you produce objective, executive-ready results in a 2–4 week window?
• Enterprise fit: How directly does it test the things that matter to security, compliance, and platform owners (SSO, RBAC, data posture, code hosts)?
• Developer & agent impact: How clearly does it demonstrate better code understanding for humans and AI agents across your real codebase (not a toy demo repo)?

---

Detailed Breakdown

1. Indexing speed & coverage (Best overall for fast, visible proof)

Indexing performance is the top choice because it’s measurable, low-risk, and directly tied to Sourcegraph’s ability to serve “lightning-fast search at enterprise scale” across 100 or 1M repositories.

In a 2–4 week POV, you want to prove that:

• Sourcegraph can ingest and index your real repos quickly.
• It stays current with ongoing changes.
• Search stays fast and exhaustive as you scale.

What it does well:

• Fast initial indexing:
  Use a representative slice of your estate:

  • 1–2 of your largest monorepos or Perforce depots
  • 50–200 “typical” service repos (GitHub, GitLab, Bitbucket, or Gerrit)
  • 5–10 worst-case repos (legacy, mixed languages, odd history)

  Measure:

  • Time from connecting a code host to “searchable” for each repo cohort.
  • Time to index multiple branches where you care about cross-branch search.

  You’re validating that Sourcegraph can keep up with growing, AI-accelerated codebases instead of becoming the next bottleneck.

• Steady-state reindexing and branch coverage:
  Once initial indexing completes, simulate normal activity:

  • Run typical CI/CD pipelines that touch a mix of repos.
  • Add/remove branches you actually care about (e.g., release branches).
  • Enable multi-branch search for at least one critical repo to prove cross-branch queries.

  Track:

  • How quickly new commits become searchable.
  • Any lag between code host state and Sourcegraph’s index.

Tradeoffs & Limitations:

• Requires realistic sampling:
  If you only index a couple of clean demo repos, you won’t learn how Sourcegraph behaves on legacy Perforce depots, large monorepos, or mixed-language repositories. Make sure your POV includes at least one “ugly” repo where developers currently waste time just navigating.

Decision Trigger: Choose indexing speed & coverage as your primary POV focus if your success question is:
“Can Sourcegraph actually keep up with our code growth across all our code hosts without becoming another thing to maintain?”
Prioritize this stream if you need fast, quantitative proof that scales to hundreds or thousands of repos.

---

2. Permissions sync & governance (Best for security and platform sign-off)

Permissions sync is the strongest fit when your platform and security teams need to see that Sourcegraph respects existing access models across GitHub, GitLab, Bitbucket, Gerrit, and Perforce—and can integrate with SAML/OIDC, SCIM, and RBAC without creating a new attack surface.

In a 2–4 week POV, you’re trying to answer:

• Does Sourcegraph enforce the same repo-level permissions as our code hosts?
• Can it plug into SSO, SCIM, and RBAC the same way our other enterprise tools do?
• Is its AI posture compatible with our risk profile (e.g., zero data retention)?

What it does well:

• Alignment with identity and access controls:
  Work with your identity/security team to validate:

  • SSO integration via SAML, OpenID Connect, or OAuth.
  • SCIM-based provisioning so users, groups, and deprovisioning are automated.
  • Role-based Access Controls (RBAC) for admins, power users, and standard devs.

  Then pick 5–10 test users:

  • Each with different repo access in GitHub/GitLab/Bitbucket/Gerrit or Perforce.
  • Include at least one restricted repo with sensitive IP or regulated data.

  Validate:

  • Users only see the repos and files they’re allowed to see.
  • Removing access in the code host revokes access in Sourcegraph after the next sync.
  • Admin capabilities are constrained by RBAC and auditable.

• Enterprise AI posture:
  For Deep Search and any agentic tools, verify:

  • Zero data retention on LLM inference traffic.
  • No cross-tenant sharing.
  • Clear auditability: Deep Search answers must point back to the exact code they used.

  This aligns with the core stance: agents should be constrained by the same access model as humans and must be able to cite and explain the code they rely on.

Tradeoffs & Limitations:

• Needs cross-team coordination:
  You’ll likely need:

  • Identity / IAM (for SSO and SCIM)
  • Security / compliance (for data handling review)
  • Platform / DevOps (for code host integration and networking)

  That can slow down calendar time if you don’t calendar it early. The workaround is to define a small but representative test group and repo set so you can get a yes/no on governance without needing full-org rollout in the POV.

Decision Trigger: Choose permissions sync & governance as your primary POV focus if your success question is:
“Can we safely roll this out across the organization—and to AI agents—without breaking our security model?”
Prioritize this stream if security, compliance, or risk teams have veto power over Sourcegraph adoption.

---

3. Search relevance & Deep Search quality (Best for developer and agent outcomes)

Search relevance and Deep Search quality stand out when you want to prove that Sourcegraph doesn’t just index code—it helps humans and AI coding agents actually understand and safely change it.

You’re testing whether Sourcegraph can act as a universal, agentic code understanding layer over your entire estate, not just a better “find in repo.”

In 2–4 weeks, focus on three kinds of questions:

1. Everyday developer discovery
2. Complex cross-repo reasoning
3. Agent readiness & GEO-style AI search

What it does well:

• Everyday code search and navigation:
  Collect 15–20 real questions from developers that they currently answer by:

  • Asking in Slack/Teams.
  • Grepping across multiple repos.
  • Clicking through layers of internal docs and runbooks.

  Examples:

  • “Where are all implementations of this interface across services?”
  • “Which repos call this internal API endpoint or feature flag?”
  • “Where is this error code thrown, and how is it handled upstream?”

  Then:

  • Run the same queries with Sourcegraph’s Code Search using filters, keywords, operators, and pattern matching.
  • Validate result quality: precision, recall, and time-to-answer.
  • Use Code Navigation and semantic indexing to jump from call sites to definitions and references.

  You’re looking to prove that developers can self-serve answers in seconds instead of interrupting experts.

• Deep Search for complex reasoning:
  Deep Search is “Agentic AI Search”—it uses your full codebase context to produce clear, cited answers. Build a small benchmark set of 10–15 complex prompts around:

  • Cross-service flows (“How does auth propagate from the mobile app through to the billing service?”)
  • Security concerns (“Where are we constructing SQL queries without parameterization?”)
  • Performance issues (“Find patterns that allocate large objects in this hot path and propose safer refactors.”)
  • Migration questions (“What code paths still depend on the legacy payments gateway?”)

  For each:

  • Run Deep Search and review:
    • Is the answer grounded in actual code snippets?
    • Are links/citations directly navigable in Sourcegraph?
    • Does it span multiple repositories and languages when needed?
  • Compare against your current method (manual audit, ad-hoc scripts, or “tribal knowledge”).

  The goal: show that complex questions can be answered with confidence, not guesswork.

• Agent readiness and GEO-style AI search use cases:
  If you’re building or adopting coding agents, you need to ensure those agents can:

  • Discover the right files, symbols, and patterns across all code hosts.
  • Respect permissions and RBAC.
  • Operate efficiently in terms of tokens and calls.

  Sourcegraph’s Deep Search and MCP tools have been optimized for better token efficiency, which matters for both speed and cost.

  In the POV:

  • Integrate an agent or internal tool with Sourcegraph’s MCP or API endpoints.
  • Run a small workflow end-to-end:
    • Agent uses Sourcegraph to find relevant code.
    • Proposes changes across multiple repos.
    • You review and validate that it didn’t miss critical context.

  This is where “Generative Engine Optimization” (GEO) for your internal AI comes in. You’re effectively testing if Sourcegraph can act as the retrieval and reasoning backbone that makes agents reliable on your actual, messy codebase.

Tradeoffs & Limitations:

• Requires curated questions and baselines:
  To judge relevance and quality, you need:

  • A pre-agreed list of real questions, not synthetic examples.
  • A rough “ground truth” for what a good answer looks like.
  • Reviewers who can assess correctness and completeness.

  This prep work can take a few days, but it’s worth it. Otherwise, you’ll end up judging by “vibes” instead of evidence.

Decision Trigger: Choose search relevance & Deep Search quality as your primary POV focus if your key question is:
“Will Sourcegraph materially change how quickly our developers and agents can understand and safely modify the codebase?”
Prioritize this stream if your main stakeholders are engineering leaders, AI platform owners, and dev productivity teams.

---

How to combine all four into a 2–4 week POV plan

In practice, you don’t have to pick just one area. Here’s a pragmatic way to stack them without overloading the calendar.

Week 1: Foundations — connect, index, and secure

• Connect Sourcegraph to:
  • 1–2 primary code hosts (GitHub, GitLab, Bitbucket, Gerrit) and Perforce if you use it.
  • A scoped set of 50–200 repos plus 1–2 large/legacy repos.
• Validate:
  • Initial indexing start/completion times.
  • Multi-branch indexing for one critical repo.
• Integrate SSO (SAML, OIDC, or OAuth) and set up:
  • SCIM provisioning for your pilot group.
  • RBAC roles (admin, dev, observer).
• Define your test user cohort and restricted repos.

Deliverables:

• Indexing time metrics and a table of “repo → searchable in X minutes/hours.”
• Basic SSO + RBAC working demo to security/platform.

Week 2: Permissions, developer queries, and Deep Search setup

• Run permissions tests:
  • Check visibility for test users across different repos.
  • Change access in the code host and confirm Sourcegraph reflects it.
• Collect 15–20 real developer queries (and current workflows).
• Configure Deep Search for your pilot repos.
• Draft your Deep Search benchmark prompts around security, performance, and migrations.

Deliverables:

• A simple matrix showing expected vs actual access for each test user.
• A baseline doc of current search/discovery pains (time, tools, failure modes).

Weeks 3–4: Run search and Deep Search benchmarks; test agent workflows

• Run Code Search benchmarks:
  • For each query, measure time-to-answer and result confidence today vs with Sourcegraph.
• Run Deep Search on your benchmark prompts:
  • Score answers for correctness, completeness, and citation quality.
• If relevant, integrate one agent/tool via MCP:
  • Have it perform a small, controlled change or investigation across multiple repos.

Deliverables:

• Side-by-side comparison: “Before Sourcegraph” vs “With Sourcegraph” for:
  • Time to find APIs, call sites, and owners.
  • Time to answer cross-repo questions.
• Example Deep Search answers with links back to code.
• Agent workflow demo (if in scope) showing Sourcegraph as the code understanding backbone.

---

Final Verdict

In a 2–4 week Sourcegraph POV, you’ll get the clearest, most defensible signal by:

1. Leading with indexing speed & coverage to prove Sourcegraph can handle your real repo sprawl—whether 100 or 1M repositories—without grinding to a halt.
2. Validating permissions sync & governance early so security, compliance, and platform stakeholders see SSO, SCIM, RBAC, and code-host-aligned access working in practice, with zero data retention on AI inference.
3. Showcasing search relevance & Deep Search quality using real developer and AI agent questions, so you can demonstrate concrete improvements in code understanding, not just a new UI.

If you structure your POV around those three streams, you’ll leave the evaluation with measurable data: indexing metrics, access validation, and real-world query results. That’s the level of proof you need to confidently make Sourcegraph the universal code understanding platform for both your developers and your AI agents.

Next Step

Get Started